MINISTRY OF HEALTH OF RUSSIAN FEDERATION SAMARA STATE MEDICAL UNIVERSITY

SAMARA REGION DEPARTMENT OF INFORMATION TECHNOLOGIES SAMARA REGION INNOVATIVE CLUSTER OF MEDICAL TECHNOLOGIES

PRESIDENTIAL GRANTS FOUNDATION NEURONET INDUSTRIAL UNION

HEALTHNET INFRASTRUCTURE CENTER IT UNIVERSE LTD

THE 5TH INTERNATIONAL CONFERENCE BCI: SCIENCE AND PRACTICE. SAMARA 2019

and the satellite conference

VIRTUAL REALITY TECHNOLOGIES IN MEDICAL AND SOCIAL REHABILITATION

Proceedings

OECD 1.02; 3.01 Proceedings of the 5th International Conference Brain-computer interface: Science & Practice. Samara 2019 and the Conference Virtual Reality Technologies in Medical and Social Rehabilitation. This publication contains abstracts of plenary and section talks and poster reports presented at the 5th International Conference BCI: Science & Practice. Samara 2019. (BCI Samara. 2019) and the Conference Virtual Reality Technologies in Medical and Social Rehabilitation. These conferences were organized on October 3-5 of 2019 by Samara State Medical University and IT Universe Ltd.. The two-days scientific part of BCI Samara.2019 included thirteen invited talks, the poster session, the panel discussion, five special symposiums and two special workshops. Among the satellite events there were three workshops opened for general public, scientific-popular lectures and Neurothlon competition. We thank all the participants and invite all the readers of this abstract collection to join us in Samara in 2020.

Organizing Committee

Alexander Kolsanov (Chair), Samara State Medical University (SamSMU) Elena Avdeeva, SamSMU Ivan Biryukov, Opportunity Technologies NPO Ilia Borishchev, IT Universe Ltd Igor Davydkin, SamSMU Stanislav Kazarin, Government of Samara region Luiza Kirasirova, SamSMU Alexander Semenov, Neuronet Industrial Union Sergei Shishkin, NRC Kurchatov Institute Irina Poverennova, SamSMU Nina Vanina, SamSMU

Program Committee

Mikhail Lebedev (Chair), Duke University School of Medicine/ NRU HSE Alexander Kaplan, Lomonosov MSU Viktor Kazantsev, Lobachevsky State University Maria Nazarova, NRU HSE Alexei Ossadtchi, NRU HSE Vasily Pyatin, SamSMU Tomasz Rutkowski, RIKEN AIP Alexander Zakharov, SamSMU Thorsten O. Zander, TU Berlin Alexander Yashkov, SamSMU © Samara State Medical University, 2019

Plenary session

Understanding, modeling and optimizing Brain-computer Interface user training

Fabien Lotte INRIA, France A prominent type of electroencephalography (EEG)-based Brain-Computer Interfaces (BCI) is Mental Imagery-based BCI (MI-BCI), with which users send commands by performing mental imagery tasks, e.g., imagined movements or mental subtraction, that are recognized by the BCI. MI-BCIs have proven promising for a wide range of applications, including communication and control for motor impaired users, gaming targeted at the general public and stroke rehabilitation, to name a few. Despite this promising potential, MI-BCIs are still scarcely used outside laboratories for practical applications. The main reason preventing MI-BCIs from being widely used is arguably their low reliability. Currently, most of BCI research aims at improving BCI reliability by improving EEG signal processing and classification algorithms. However, another key element of the BCI loop should be considered to improve their reliability: the users themselves. Indeed, MI-BCI control is known to be a skill that needs to be learnt. The more users practice to control the MI-BCIs, the better they can learn to control them and thus the better the reliability of the system. An additional promising direction to improve the reliability of MI-BCIs is thus to improve user training. Unfortunately, why some users managed to learn to control MI-BCIs whereas some other do not or how to favor this learning, is still rather poorly understood. Thus, it appears as necessary to understand and to model MI-BCI user learning processes, to be able to improve them, based on these models. In this talk, I will thus present our work towards modeling, both at the theoretical, conceptual and computational levels, MI-BCI user training and performances. In particular, I will describe our experimental studies and methodological contributions to identify factors that impact BCI user training, such as BCI users skills, traits or states, the impact of the environment (including BCI experimenters themselves) or of the feedback used. I will also describe our efforts to model, both conceptually and computationally, how these factors interact and can be used to predict successful BCI performances and learning. Finally, I will briefly illustrate how we could leverage the knowledge gain from these models to improve MI-BCI user training, by providing better feedbacks or training tasks, adapted to each user.

Combining the eye gaze, EEG and MEG for creating new modes of humancomputer interaction

Sergei L. Shishkin

NRC Kurchatov Institute, Russia Brain-computer interfaces (BCIs) are developed mainly as a technology that may assist people with motor disabilities. They also attract certain interest in a more general sense, as a possible tool for fast and fluent translation of intentions into actions: for example, it could be

used in the studies of the physiological basis of will, and possibly even be helpful in applications supporting creative work. However, the performance of the existing noninvasive BCIs is too low, while the invasive BCIs are associated with high risk and cost. Thus, it is currently difficult to study the hypothetical benefits of the fluent, direct translation of intentions into actions, or other hypothetical emergent features of the effective direct brain-computer interaction. Uncertainty about the possible benefits leads to insufficient motivation of creating new, highly capable technologies of interacting with computers beyond the assistive technologies. Gaze-based human-machine interaction based on the eye tracking is an interesting model of fluent human interaction with computers. However, when the response of a gaze interaction system is sufficiently fast to make interaction perceived as fluent, frequent errors are inevitable, because the system cannot differentiate the gaze commands from spontaneous gaze behavior (Jacob, 1990). On-the-fly classification of gaze fixations into spontaneous ones and those intentionally used to trigger the interface could be based on the electroencephalogram (EEG) signal (Protzak et al., 2013; Shishkin et al., 2016). To provide fluent interaction, the eye-brain-computer interface (EBCI), however, should provide high classification performance using short segments of data. In our first attempts to make experiments with an online EBCI that classified gaze fixations using 300 ms long EEG segments false positives appeared frequently, so fluent interaction could not be achieved (Nuzhdin et al., 2017). We currently study if the inclusion of the magnetoencephalography (MEG) into the hybrid EBCI may help to make on-the-fly detection of the intentional gaze fixation effective enough for making interaction fluent. The first attempts to classify spontaneous and intentional gaze fixations using the MEG signal showed that solving this problem might not be easy (Ovchinnikova et al., in prep.). However, statistical comparison of the MEG time-locked to spontaneous and intentional gaze fixations demonstrated that intentional fixations are indeed accompanied by specific brain activity. Certain MEG components became strongly pronounced in the intentional fixations well before the time when the EEG intention markers (Shishkin et al., 2016) where observed (Vasilyev et al., in prep.). Thus, with the improvement of classification methodology a MEG-based online EBCI may become feasible. Its performance might be further enhanced by classifying co-registered EEG, possibly enabling, for the first time, the studies of the new modes of fluent human-computer interaction. This work was supported by the grant 18-19-00593 from the Russian Science Foundation. Jacob R.J.K. (1990) Proc. SIGCHI Conf. Human Factors in Comp. Sys., 11-18. Nuzhdin et al. (2017) Proc. 7th Graz BCI Conf., 361-366. Protzak J. et al. (2013) Proc. Int. Conf. UAHCI, 662–671. Shishkin S.L. et al. (2016) Front. Neurosci. 10:528.

Brain/Neural-Machine Interfaces (B/NMIs) for Restoration of Movement and Beyond

Surjo Soekadar

University of Tuebingen, Charité - University Medicine Berlin, Germany Implementation of neural control in the application of advanced robotic systems promises restoration of autonomy and quality of life in severe paralysis. One of the main challenges to integrate such systems in everyday life environments relates to the insufficient reliability and

user-friendliness of brain-control, particularly when brain signals are recorded non-invasively. This talk will introduce a number of strategies to overcome this challenge, e.g., integration of vision-guidance and context-sensitivity, fusion of brain control with other bio-signals and minimization of recording electrodes. We argue that availability of B/NMI assistive technology that is applicable in every-day life environments will not only have an immediate impact on quality of life and autonomy in severe paralysis, but could also trigger neural recovery when used on a daily basis. Importantly, this principle may also translate to other brain disorders. Here, combination of B/NMI technology with closed-loop non-invasive brain stimulation (NIBS) may proof particularly useful. Most recent advances in this endeavor will be presented and discussed.

From direct control to neuroadaptivity: The use of Brain-Computer Interfaces for Human-Machine Systems

Dr. Thorsten O. Zander

Berlin Institute of Technology, Germany Zander Laboratories, Amsterdam, The Netherlands In my talk I will provide an overview of recent developments how BCIs can be applied in Human‐ Machine Systems, specifically for users without disabilities. Next to direct control paradigms – which might find application in specific use cases – Passive BCIs have proven to be a powerful tool to provide information to technical systems without the need for any additional attention or effort by the user. Passive Brain‐Computer Interfaces (pBCIs, [1]) can assess information about changes in cognitive and affective state in real time and convey an interpretation of these states as implicit commands [2] to a machine. The machine can then automatically adapt its own state to support a given task in the Human‐Machine System [3]. Furthermore, by collating information about the user state with the task‐specific context and using methods from machine learning and artificial intelligence a user model can be generated that even reflects correlates of higher cognition [4]. The resulting Neuroadaptive Technology leads to a convergence of human and machine intelligence and enables a fundamentally new way of interaction with technology [4, 5, 6]. I will give brief examples for each of the above‐mentioned technical approaches and discuss the hurdles that need to be taken to bring Neuroadaptive Technologies into our daily lives. References: [1] Zander, T. O., & Kothe, C. (2011). Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. Journal of neural engineering, 8(2), 025005. [2] Zander, T. O., Brönstrup, J., Lorenz, R., & Krol, L. R. (2014). Towards BCI-based implicit control in human– computer interaction. In Advances in Physiological Computing (pp. 67-90). Springer, London. [3] Zander, T. O., Kothe, C., Jatzev, S., & Gaertner, M. (2010). Enhancing human-computer interaction with input from active and passive brain-computer interfaces. In Brain-computer interfaces (pp. 181-199). Springer, London. [4] Zander, T. O., Krol, L. R., Birbaumer, N. P., & Gramann, K. (2016). Neuroadaptive technology enables implicit

cursor control based on medial prefrontal cortex activity. Proceedings of the National Academy of Sciences, 113(52), 14898-14903. [5] Lorenz, R., Monti, R. P., Violante, I. R., Anagnostopoulos, C., Faisal, A. A., Montana, G., & Leech, R. (2016). The automatic neuroscientist: a framework for optimizing experimental design with closed-loop real-time fMRI. NeuroImage, 129, 320-334. [6] Iturrate, I., Chavarriaga, R., Montesano, L., Minguez, J., & Millán, J. D. R. (2015). Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control. Scientific reports, 5, 13893.

Clinical and neurotechnological aspects of neural dynamics in EEG/MEG recordings

Vadim V. Nikulin

Max Planck Institute for Human Cognitive and Brain Sciences, Germany Institute for Cognitive Neuroscience, HSE University, Russian Federation Compared to spatial synchronization, temporal dynamics of neural activity only recently gained a widespread attention in neuroscience. A particularly interesting discovery was a demonstration of Long-Range Temporal Correlations (LRTC) in the amplitude dynamics of neural oscillations recorded with EEG/MEG. LRTC might indicate a presence of a critical state in neural dynamics which was previously shown to be beneficial for the optimal processing of information in the brain. In this talk I will review studies showing a relevance of LRTC for cognitive, motor tasks and neurotechnology (BCI). Moreover, it was shown that LRTC could serve as clinical biomarkers sensitive to pathological neuronal activations in Schizophrenia, Depression and Parkinson’s Disease. We then argue that since a specific range of LRTC is associated with the optimal functioning of neuronal networks, an amplitude modulation of oscillations through the neurofeedback can be used for the therapeutic purposes. Finally in this talk, an idea of a neurofeedback for the modulation of corticomuscular interactions will be introduced as a possible strategy for the recovery of motor control after stroke.

Brain-computer interface technology: pragmatic and philosophical aspects

Alexander Kaplan Lomonosov MSU, Russia Scientific and technological issues of creating and improving brain-computer interfaces (BCI) have been discussed with increasing interest for more than half a century. Only since the beginning of this century, the number of papers published in leading scientific journals regarding BCI has increased almost 100 times! Impressive results have been achieved in decoding and translating brain commands to a variety of external actuators and processes, from text printing and manipulator control, to speech synthesis and visualization of mental images. However, to date, neurointerface technologies remain the subject of laboratory research and development, with virtually no sector in production processes, communications and services. The first tentative steps neurointerfaces do only in the field of medical

rehabilitation, to compensate for the lost of communication (in particular, "NeuroChat" in Russia) and for recovery of motor functions (“Exohand” in Russia). Why is it that futuristic predictions about the widespread use of BCI and the transition of a person to the level of full integration of his brain with the computer still do not come true?

Deep Learning for Brain Signals: Performance and Interpretability

Tonio Ball

University of Freiburg, Germany Deep learning with artificial neural networks is rapidly gaining attention in the brain-computer interfacing (BCI) field. Deep networks are increasingly used for the analysis of brain signals in this context, in particular of the EEG. This is at least partially motivated by the great success of deep learning (DL) in other domains such as computer vision. Here, I summarize our own work on deep learning for EEG as well as the state of the field now at the end of 2019. First, with respect to performance, currently, there is no clear evidence that DL has better performance than traditional methods for EEG decoding. The other way around, however, there is also no evidence that any traditional method would systematically outperform DL. Importantly, DL achieves competitive performance when trained end-to-end on raw EEG. I argue that the availability of a method that can be trained on raw EEG has great importance, not only for practical application, but also as a novel tool for neuroscientific research, enabling for example novel possibilities for discovery of a priori unknown informative brain signal features. I conclude by showing recent findings from visualizations of what networks learn from EEG, and how EEG is internally represented in trained networks.

Symposiums

Simposium State-of-the art neurotechnologies in real healthcare and rehabilitation

Individualized Neurofeedback training reduces Theta/Beta more efficiently than

standard, non-individualized.

Olga M. Bazanova 1, Elena Sapina 2

1Novosibirsk, Russia (Laboratory of Affective, Cognitive and Translational Neuroscience, Federal State Research Institute of Physiology and Basic Medicine), 2Novosibirsk, Russia (Laboratory of Biofeedback Computer System, Research Institute of Molecular Biology and Biophysics)

INTRODUCTION Neurofeedback training (NFT) to decrease the theta/beta ratio (TBR) has been used in the treatment of hyperactivity and impulsivity aspect of the attention deficit hyperactivity disorder (ADHD), but its efficiency is still debatable. Main reason of the non-effectivity could be the lack of neurobiological understanding of the disorder. The first aim of this study was to determine the most predictive EEG and EMG biomarkers of ADHD. The second – to compare efficiency NFT organized with individually determined and standard EEG frequency bands. We hypothesized that NFT to decrease individually adjusted TBR and muscle tension simultaneously is more efficient than NFT to decrease TBR according to standard EEG frequency bands.

MATERIALS AND METHODS We recruited 106 children diagnosed with ADHD (ADHD) and 21 healthy controls (HC), all male and aged between 6 and 9. First, we had identified potential psychometric and EEG biomarkers for ADHD, impulsivity and attention were assessed with Go/no-Go task and delayed gratification task, respectively; 19-channel EEG and forehead muscles EMG were recorded. Then, the ADHD children were randomly asigned into (1) standard, (2) individualized, (3) individualized+EMG and (4) sham NFT (control) groups. The groups were compared based on TBR and EEG alpha activity both within and across training sessions, as well as hyperactivity and impulsivity three times: pre-NFT, post-NFT and six months after the NFT.

RESULTS Individual alpha peak frequency(IAPF), alpha bandwidth and alpha amplitude suppression in response to eyes open in ADHD children were decreased, as well as alpha1/alpha2 (a1/a2) ratio and scalp muscle tension increased when compared with healthy peers (ŋ²≥0.212). All contingent TBR NFT groups exhibited significant NFT-related decrease in TBR not evident in the control group. Moreover, we detected a higher overall alpha activity in the individualized but not in the standard NFT group. Mixed MANOVA considering between-subject factor GROUP and within-subject factor TIME resulted in that the combined individualized EEG and EMG training led to the highest level of decrease in impulsivity and attention deficit associated with increase in the individual alpha activity at the six months follow-up when comparing with the other approaches (post hoc t = 3.456, p=0.011).

CONCLUSION This study identified individual alpha activity as the ADHD biomarker with the highest predictive power and demonstrated that individualized TBR NFT reduces TBR more efficiently and successfully than standard, non-individualized NFT; regardless of relevant clinical considerations.

The results of BCI-controlled Exoskeleton clinical trials: rehabilitation outcome and EEG analysis

Pavel Bobrov1,2*, Alexander Frolov1,2, Guzel Aziatskaya3, Elena Biryukova1,2, Yulia Bushkova2,

Anna Kondur4, Roman Lyukmanov3, Lidiya Turbina4, Sergei Kotov4

1 Institute of Higher Nervous Activity and Neurophysiology, RAS, Moscow; 2 Pirogov Russian National Research Medical University, Moscow; 3 Research Center of Neurology RAS, Moscow; 4M.F. Vladimirsky Moscow Regional Research and Clinical Institute, Moscow. *E-mail: p-bobrov@yandex.ru

INTRODUCTION The report describes the results of placebo-controlled, multi-center clinical trials of BCI-controlled Exoskeleton assisted rehabilitation procedure for post-stroke patients with upper limb paresis.

MATERIALS AND METHODS Of 644 patients screened 429 did not meet the study inclusion criteria. The remaining 215 patients were split into three groups: main (n=116), placebo (n=59), and control (n=40). The motor function recovery was assessed prior and after intervention both with clinical scales (FMMA, ARAT, MRCSS) and instrumentally using motion tracker. The EEG recordings obtained during the BCI and placebo sessions were analyzed using several ICA, BSS and CSP decomposition methods. The sources of EEG activity, most relevant for the BCI performance were identified and clustered using the attractor neural network with increasing activity. For the patients with structural MRI recordings and digitized EEG electrode positions available the sources of EEG activity were localized by solving the EEG inverse problem. The results of the EEG analysis were compared to those obtained for the healthy subjects.

RESULTS Significantly better motor function recovery was observed in the main group compared to the control and placebo. In patients with acute stroke the significantly higher improvement was observed both for proximal and distal domains, while in patients with chronic stroke the improvement was significantly higher only in distal domain. The results of EEG analysis show that in most cases the BCI sessions stimulate activity of, primary somatosensory areas, supplementary motor area, precuneus, premotor areas. EEG rhythm desynchronization during motor imagery in patients resembles in general that reaction In healthy subjects, but the patients exhibit significantly lower and much more variable rhythm peak frequencies.

CONCLUSION Adding a BCI+Exonand sessions to routine post-stroke rehabilitation of motor function allows to significantly improve its outcome due to feedback stimulation of the brain areas involved in involved in motor planning, execution, and imagining.

Recovery of post stroke motor function with hand exoskeleton controlled by

brain-computer interface: effect of repeated courses

Anna Kondur1, Elena Biryukova2,3,Alexander Frolov2,3, Pavel Bobrov ² ³, Sergey Kotov1

1Vladimirsky Moscow Regional Research Clinical Institute, Moscow, Russia, annasams@mail.ru, kotovsv@yandex.ru 2Institute of Higher Nervous Activity and Neurophysiology, RAS, Moscow, Russia, ebiryukova@mail.ru, aafrolov@mail.ru, p-bobrov@yandex.ru 3Pirogov Russian National Research Medical University, Moscow, Russia INTRODUCTION Clinical studies of effectiveness of post stroke rehabilitation using hand exoskeleton controlled by brain-computer interface (BCI) are quickly developed last decade [1]. In Russian Federation rehabilitation procedure BCI+exoskeleton has been clinically tested [2,3,4] and is currently put in clinical practice. In the present study we analyze the effect of intensive repeated rehabilitation courses on motor function (MF) progress.

MATERIALS AND METHODS Biomechanical analysis of patient’s movements registered before and after each rehabilitation course as well as common clinical scales Fugl-Meyer and ARAT were used for MF assessment [5,6]. Biomechanical parameters used for MF assessment were 1) standard deviation of angular velocities in each seven degree of freedom of the arm and 2) joint individuation. The first parameter was considered as an assessment of muscle forces and the second as an assessment of joint coordination.

RESULTS Significant MF improvement progressively increased during two subsequent courses despite of some decrease between them was obtained both by biomechanical analysis and clinical scales assessment. Arm’s degrees of freedom affected during first and second courses were different, testifying the progress of neuroplasticity stimulation. MF progress compared with initial MF state was greater in patients with severe paresis than in patients with moderate one.

CONCLUSION Repeated courses of BCI+exoskeleton rehabilitation contribute in MF progress for patients in the late post stroke period and for patients with severe paresis. Obtained data can be useful for optimization of protocol of BCI+exoskeleton procedure.

REFERENCES [1] Monge-Pereira E., Ibañez-Pereda J., Alguacil-Diego I.M. et al. Use of electroencephalography brain-computer interface systems as a rehabilitative approach for upper limb function after a stroke: a systematic review // PM&R. 2017. V.9. №9. Р. 918.

[2] Иванова Г. Е., Бушкова Ю.В., Суворов А.Ю. и др. Использование тренажёра с многоканальной биологической обратной связью «ИМК + экзоскелет» в комплексной программе реабилитации больных после инсульта // Журнал Высш. Нервн. Деят. 2017. Т.67. №4. С.464. [3] Котов С.В., Турбина Л.Г., Бобров П.Д. и др. Реабилитация больных, перенесших инсульт, с помощью биоинженерного комплекса «интерфейс мозг - компьютер + экзоскелет» // Журнал неврологии и психиатрии им. С.С. Корсакова. 2014. Т.12. С.66. [4] Фролов А.А., Мокиенко О.А., Люкманов Р.Х. и др. Предварительные результаты контролируемого исследования эффективности технологии ИМК–экзоскелет при постинсультном парезе руки // Вестник РГМУ. 2016. Т.2. С.17. [5] Бирюкова Е.В., Павлова О.Г., Курганская М.Е. и др. Восстановление двигательной функции руки с помощью экзоскелета кисти, управляемого интерфейсом «мозг-компьютер». Случай пациента с обширным поражением мозговых стрeктур // Физиология человека. 2016. Т. 42. №1. С. 19. [6] Кондур А.А., Бирюкова Е.В., Котов С.В. и др. Кинематический портрет пациента как объективный показатель состояния двигательной функции в процессе нейрореабилитации с использованием экзоскелета руки, управляемого интерфейсом мозг-компьютер // Ученые записки Санкт-Петербургского медицинского университета им. И.П. Павлова. 2016. Т. 23. № 3. С. 28.

TMSmap software for quantitative analysis of TMS mapping results - demonstration of the new features

Pavel Novikov1*, Maria Nazarova1,2, Kseniya Kozlova1, Ekaterina Ivanina3, Vadim Nikulin1,4,5

1Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia; 2Federal Center for Cerebrovascular Pathology and Stroke, The Ministry of Healthcare of the Russian Federation, Federal State Budget Institution, Moscow, Russia; 3Department of Psychology, National Research University Higher School of Economics, Moscow, Russia; 4Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; 5Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité—Universitätsmedizin Berlin, Berlin, Germany *E-mail: novikov-p-a@yandex.ru Keywords: transcranial magnetic stimulation, cortical mapping, software

We have developed a new version of free standalone software for standardising quantitative analysis of the data obtained with transcranial magnetic stimulation (TMS) mapping procedure – TMSmap (a previous version is described in [1], http://tmsmap.ru/)). The program allows estimating standard TMS cortical representation parameters such as area, volume, locations of the hotspot and centre of gravity, as well as excitability profile, the overlap between the cortical representations and other user-defined parameters. New version of the program has an option for co-registering TMS mapping data to Montreal Neurological Institute (MNI) space and thus allowing group comparison of the TMS mapping

results. For convenient group analysis it is possible to load data and compare TMS maps automatically using a predefined script. New version of TMSmap also includes the possibility to analyse only stimulation points corresponding to a specific brain structure visible on MRI image, for example analysing only the map based on the stimuli applied in the vicinity of the central sulcus. Input data for the software includes the coordinates of the coil position or calculated locations of the induced electric field (depending on the neuronavigation system), structural MRI data and a response at each point of stimulation (motor evoked potentials, behavioural response etc.). TMSmap was developed for the versatile assessment and comparison of the cortical maps following different experimental interventions including but not limited to longitudinal studies (e.g., studying of cortical reorganization during rehabilitation after stroke). Acknowledgements: the work was supported by Russian Science Foundation under grant 18-79-00328.

REFERENCES [1] Novikov P.A., Nazarova M.A., Nikulin V. V. TMSmap – Software for Quantitative Analysis of TMS Mapping Results // Front. Hum. Neurosci. Frontiers, 2018. Vol. 12. P. 239.

Using light to monitor brain activity from the macro- to the nanoscale

M. Rosendale1*, F. Lotte2 1Univ. Bordeaux; 2 Inria Bordeaux SudOuest LaBRI (Univ. Bordeaux / CNRS / Bordeaux INP) *E-mail: morgane.rosendale@u-bordeaux.fr This commentary observes that most BCIs solely rely on the electrical activity of populations of neurons while other fields of neuroscience monitor neuronal activity via a wealth of other means that could inspire BCI research. Electroencephalography, electrocorticography and microelectrode arrays record the mean electric activity of millions, thousands or a few neurons respectively with signal to noise increasing with invasiveness. An even lower scale, seldom used for BCIs, but widely used in animal research are intracellular recordings (patch-clamp or nanowires). These modalities not only record the all-or-nothing nature of an action potential as the above, they grant access to sub-threshold electrical activity. Neuronal communication however is more than electrical impulses. Neurons are cells full of proteins and metabolites that emit and receive chemicals. To grasp this complexity, neurobiologists use light rather than electricity [1]. Proteins can indeed be engineered to emit fluorescence under chosen conditions. Directly detecting currents are voltage sensors: proteins inserted in the neuronal membrane that shine light proportionally to the membrane potential. More widely used are calcium sensors: depolarised neurons release intracellular calcium. Action potentials are thus easily detectable as flashes of light. Calcium imaging has been used in animals at all scales, from subcellular to cellular, network and even transcranial. One can also access the chemical nature of firing neurons: either by sensing neurotransmitters or by genetically targeting a chosen population, e.g. excitatory, inhibitory or neuromodulatory neurons. The refined information obtained from genetics and protein engineering could thus complete electrical data as input to a BCI to increase its accuracy.

REFERENCES [1] Griesbeck O., Curr Opin Neurobiol. 2004 Oct;14(5):636-41. Fluorescent proteins as sensors for cellular functions.

Invasive and non-invasive brain mapping in clinical practice

Sinkin M.V.1,2*, Ossadtchi A.E.3

1 N.V. Sklifosovsky Research Institute for Emergency Medicine. Moscow 2A.I. Evdokimov Moscow State University of Medicine and Dentistry. Moscow 3National Research University “Higher School of Economics” E-mail: mvsinkin@gmail.com INTRODUCTION Localization of functions in the cerebral cortex and determination of the location of conductive pathways is widely used in clinical disciplines related to diseases of the central nervous system and subsequent rehabilitation. During neurosurgical operations, this is necessary to prevent iatrogenic tissue damage and the emergence of a new neurological deficit. Direct electrical stimulation mapping of brain functions has been used during neurosurgical operations for the past 100 years, and this method remains the gold standard [1,2]. Functional magnetic resonance imaging (fMRI) and suggestive transcranial magnetic stimulation (nTMS) are not invasive mapping methods, but the effect of brain displacement after the opening of the dura mater significantly reduces the value of these studies [3]. Among invasive techniques, passive mapping of cortical functions based on the analysis of electrocortical cortical data (EcoG) during the performance of different paradigms by the patient is a promising approach [4].

METHODS We have created a mobile hardware-software complex to determine the location of the speech zone on the basis of the analysis of the desynchronization of the high-frequency range, which occurs at the moment when the patient pronounces the images appearing on the tablet computer screen. The results were compared with direct stimulation of the speech cortex in a classical way.

RESULTS Exact coincidence of the speech center localization determined by the analysis of EcoG changes and electrostimulation performed by the classical W.Penfield method was revealed.

CONCLUSION Practical possibility of localization of cortical representation of speech eloquent zone according to EcoG data expands the list of methods of functional mapping of the brain, and due to high accuracy and absence of complications increases the number of patients who can conduct this study. REFERENCES [1] Penfield W., Boldrey E. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain 1937;60(4):389-443. doi:10.1093/brain/60.4.389 [2] So E.L., Alwaki A. A Guide for Cortical Electrical Stimulation Mapping. J Clin Neurophysiol 2018;35(2):98-105. doi:10.1097/WNP.0000000000000435

[3] Poydasheva A.G., Bakulin I.S., Chernyavskiy A.Yu et al. Motor cortex mapping with navigated transcranial magnetic stimulation and its clinical application. Medical Alphbet. 2017;2(22):21-25. [4] Tallon-Baudry C., Bertrand O. Oscillatory gamma activity in humans and its role in object representation. Trends Cogn Sci 1999;3(4):151-162. PMID: 10322469

Decoding movement direction from ECoG for the instructed-delay center-out task

performed with a pen

Ksenia Volkova*, Alexei Ossadtchi, Alexander Belyaev, Mikhail Lebedev

Center for Bioelectric Interfaces, NRU Higher School of Economics, Moscow, Russia * Email: voxxys@gmail.com Keywords: ECoG, BCI, movement decoding, direction decoding, handwriting INTRODUCTION Previous studies have shown that movement parameters can be decoded from electrocorticographic (ECoG) recordings, which makes ECoG a useful approach for implementing brain-computer interfaces (BCIs) ([1], [2]). Yet, it is not well understood how ECoG-based BCIs could be employed for enabling fine hand movements, such as drawing and handwriting. In this study we utilized a center-out task performed with a pen to investigate representation of hand movement direction in multichannel ECoG.

MATERIALS AND METHODS ECoG recordings were conducted in epileptic patients undergoing examination in a neurological clinic. All experimental procedures were approved by the Institutional ethical committee. Cortical activity was recorded with either ECoG grids or strips implanted for 2-5 days. The experimental task was implemented in the open-source NFB lab software developed in our laboratory. ECoG activity was sampled at 2048 Hz. As the experimental paradigm, we used the famous center-out task introduced by Georgopoulos et al. [3], with the difference that our subjects executed fine movements with a hand-held pen on digitizing tablet rather than reaching with their arms. Each trial started with the subject holding the pen over a central target; after a delay, a peripheral target was shown; when the central target disappeared, the subject moved the pointer to the peripheral target. We analyzed representation of movement direction in ECoG for both the delay and movement periods of the task.

RESULTS Spectral band plots revealed changes associated with target onset, movement trigger and movement execution such as movement-related attenuation the mu band accompanied by activity increase in the gamma band at the movement onset, which quantitatively depended on movement direction. To identify features encoding movement direction, we performed pairwise statistical comparisons for the signals corresponding to eight directions incorporated in the task. The high gamma band (70-150 Hz) was particularly sensitive to movement direction for the movement initiation and execution periods. In some cases, directionally selective activity could be detected as early

as 500-700 ms before the movement onset. In addition, practically all bands in the 4-100 Hz range contained directional information for both spatial and temporal domains of the ECoG data. Regions of higher correlation show smooth topographies over grid channels. Decoding direction from ECoG proved to be successful. Thus, SVM classifier applied to gamma-band features yielded accuracy up to 65% (with the chance level of 12.5%). We suggest that this benchmark could be exceeded with more advanced decoding algorithms.

CONCLUSION The center out-task performed with a hand-held pen proved to be a useful approach for both studying the encoding of motor parameters by ECoG activity and decoding movements from ECoG. We propose that that fine motor tasks, such as drawing and handwriting, be employed in ECoG studies because of their essential dependence on cortical control. Our demonstration of decoding movement direction from ECoG can be taken as an evidence that ECoG-based BCIs could become clinically relevant for restoration and rehabilitation of fine motor skills in patients suffering from neurological conditions. However, we still have to demonstrate a reliable real-time BCI control performed in the absence of overt movements -- the study that is currently undergoing in our laboratory. Additionally, the complex nature of ECoG patterns for the instructed-delay, center-out task suggests that advanced analyses (e.g., solving the inverse problem for ECoG) and tasks (e.g., mental rotation) should be employed in the future to fully elucidate the underlying neural mechanisms and their utility for neuroprosthetic applications.

REFERENCES: [1] Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G., and Moran, D. W. (2004). A brain–computer interface using electrocorticographic signals in humans. Journal of neural engineering 1, 63 [2] Ball, T., Schulze-Bonhage, A., Aertsen, A., and Mehring, C. (2009). Differential epresentation of arm movement direction in relation to cortical anatomy and function. Journal of neural engineering 6, 016006 [3] Georgopoulos, A. P., Kalaska, J. F., Caminiti, R., and Massey, J. T. (1982). On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. Journal of Neuroscience 2, 1527–1537

Preliminary results of the research of efficiency of the technique of virtual reality for restoration of motive function of the lower extremities at patients in the sharp period

of the stroke A.V. Zakharov*, E.V. Khivintseva, V.F. Pyatin, A.V. Kolsanov, M.S. Sergeeva

Samara State Medical University

*E-mail: zakharov1977@mail.ru KEYWORDS: stroke, immersive virtual reality, rehabilitation, lower extremities.

INTRODUCTION Currently, stroke is the main cause of death and the third most frequent cause of disability worldwide [1]. Disability in stroke is caused by impaired motor function of the upper and lower limbs [2]. It is believed that the leading cause of disability in patients with stroke is motor

disorders of the upper extremities. At the same time, the restoration of stato-locomotor function of the lower extremities is the earliest goal of motor rehabilitation contributing to a significant expansion of rehabilitation measures in the future [3]. To study the efficiency of usage of immersive virtual reality with proprioceptive sensory effects for the rehabilitation of the static locomotor function of patients in the acute period of ischemic stroke.

MATERIAL AND METHODS The study involved 33 patients in the acute period of ischemic stroke in the carotid system. The patients were randomized into two groups, the study group additionally received classes under immersive virtual reality with a sensory impact. The course consisted in 10 classes of 15 minutes each.

RESULTS According to the Berg balance scale, on the 6th day of classes (p = 0.03), an improvement in the static-locomotor function was detected in the study group. On the last day of the rehabilitation course, the patients from the group subjected to the course demonstrated the improvement in the static locomotor function by 23 points (95% CI 13-27 points) in the study population and by 7 points (95% CI 2-13 points) on the Berg balance scale, as compared with the control group.

CONCLUSION Our preliminary results demonstrate a positive effect of usage of immersive virtual reality tools in rehabilitation of static-locomotor function. Also, this research demonstrates the safety of this method for patients in the acute period of ischemic stroke. Further increase of the efficiency of usage of immersive virtual reality is possible due to multi-touch feedback or neurocomputer interface technology.

REFERENCES: [1] Roger VL, et al. Heart disease and stroke statistics–2011 update: a report from the American Heart Association. Circulation. 2011;123(4):18–209. [2] Wang W, et al. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults. Circulation. 2017;135(8):759–71. [3] Prabhakaran S, et al. Inter-individual variability in the capacity for motor recovery after ischemic stroke. Neurorehabil Neural Repair. 2008;22(1):64–71.

Neurophysiological effects of a motor training with game feedback based on Brain-Computer Interface “i-BrainTech”.

Zhanna Nagornova1,2*, Philip Gundelakh2,3, Natalia Shemyakina1,2, Lev Stankievich2,3, Konstantin Sonkin2

1Sechenov institute of evolutionary physiology and biochemistry, Russian academy of sciences, St. Petersburg, Russia; 2LLC “I-Brain”, St. Petersburg, Russia; 3Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia * Email: nagornova_zh@mail.ru

The technology of a brain computer interfaces (BCI) based on motor imagination is a perspective for development of rehabilitation systems for patients with motor function

impairment due to central nervous system damage such as stroke. The aim of the study was the assessment of the BCI application. The post stroke volunteers participated in the study. Trainings consisted of game sessions in which the imagined movement of right or left hand was decoded by the elaborated classifier committee based on artificial neural networks and support vector machines. In case of the correct movement recognition, the hand of an avatar in the game moved for gathering apples. Each volunteer had from 8 to 17 training sessions. During the training EEG was recorded from 19 Ag/AgCl electrodes by means of the smartBCI (Mitsar, SPb). One training game lasted 5 min and included about 150 trials (1200 ms each) with imagined movements. For calculation of time-frequency maps and analysis of the event related EEG synchronization/ desynchronization (ERS/ERD) the wavelet transform (Morlet wavelet) was used. The ERS/ERD were calculated in the narrow frequency ranges (with the 2Hz step) in four 300 ms time intervals. The individual dynamics of the ERS/ERD was estimated by means of linear regression analysis. Changes of the ERS/ERD during a course of trainings were observed in all participants, the larger number of changes during training in most of participants was observed in the damaged hemisphere. Increase of the ERD in the alpha frequency (10-12Hz) and increase of the ERS in the high-frequency bands (18-30Hz) were the main effects. Thus, the BCI trainings lead to the EEG changes related to the increase of the excitability of sensorimotor cortex during the movements imagination.

RFBR 16-29-08296 ofi-m

Symposium

Machine Learning and Deep Neural Networks in Neurophysiology and Healthcare

Psychoneurological disorders diagnostics based on MRI data

Ekaterina Kondrateva*, Marina Pominova , Maksim Sharaev, Evgeny Burnaev, Alexander Bernstein

Skolkovo Institute of Science and Technology, Moscow, Russia *E-mail: ekaterina.kondrateva@skoltech.ru

INTRODUCTION There is a need for accurate objective diagnosis in neurology and psychiatry, then MR imaging is considered one of the most powerful diagnostic tools applicable for multiple examinations and in children. In terms of machine learning, prognostic tools for MRI can be observed as classification (segmentation) models built on dataset of healthy subjects and pathology, see [1] - [4]. There are two types of models: deep learning networks [5], [6] used for full size images and canonical machine learning (ML) models for lower dimensional features, extracted from the imagery.In this work we explore the potential of convolutional neural networks (CNNs) as well as canonical ML models on extracted features for two psychoneurological disorders classification.

MATERIALS AND METHODS We consider two classification problems: two main binary classifications from one source: 49 Bipolar Disorder patients and 50 Schizophrenia patients vs 122 Healthy Controls. For each dataset the goal was to predict whether a subjectis from pathology group or healthy control group. The model, called VoxResNet [6] with 3D convolutional layers was used for full size data classification with and without preprocessing. As data preprocessing stage, brain images were normalized to standard space, which implies standardization and alignment to common space for brain volumetric analysis during standard preprocessing protocol FreeSurfer-derived segmentations of the cortical gray-matter in fmriprep toolbox. After normalization, MR images were segmented to extract 927 numerically describing brain anatomy (brain region volume, curvature, etc.). The Baseline performance was calculated with Support Vector Classifier (SVC) model was SVC, C = 10, kernel = rbf, gamma = 0.01 implemented in sklearn. Classification results for MRI data with no preprocessing, normalized images and morphometric features were compared. RESULTS For Schizophrenia classification deep neural methods outperform the baseline classifier only for unprocessed data. Then, the baseline classification AUC is 0.739 (0.086). For Bipolar Disorder considered convolutional networks predictive power is compatible with the baseline score 0.668 (0.074), independently of the data preprocessing.The results for both classification models are represented in Table 1.

Table 1: Binary classification of schizophrenia (50) / control (122) and of Bipolar Disorder (49) / control (122). voxresnet model with and without d-convolutionallayers on different prepossessing stages. validated on 3-fold CV with 3 repeats, ROC/AUC scoring

CONCLUSION We show that Bipolar Disorder and Schizophrenia could be classified on structural MRI. The best classification accuracy for both disorders is being 67% as best score for Bipolar Disorder and 79% for Schizophrenia. Confirming that there are structural biomarkers or measurable indicators in brain structure of the considered disorders and worth of further investigation.

REFERENCES [1] B. C. Bernhardt, S.-J. Hong, A. Bernasconi, and N. Bernasconi, “Mag-netic resonance maging pattern learning in temporal lobe epilepsy:classification and prognostics,”Annals of neurology, vol. 77, no. 3,pp. 436–446, 2015.

[2] E. Hosseini-Asl, G. Gimel’farb, and A. El-Baz, “Alzheimer’s disease diagnostics by a deeply supervised adaptable 3d convolutional network,”arXiv preprint arXiv:1607.00556, 2016. [3] M. Pominova, A. Artemov, M. Sharaev, E. Kondrateva, A. Bernstein,and E. Burnaev, “Voxelwise 3D convolutional and recurrent neural networks for epilepsy and depression diagnostics from structural and functional MRI data,” inIEEE International Conference on Data MiningWorkshops, ICDMW, vol. 2018-November, pp. 299–307, IEEE, nov2019. [4] S. Ivanov, M. Sharaev, A. Artemov, E. Kondratyeva, A. Cichocki,S. Sushchinskaya, E. Burnaev, and A. Bernstein, “Learning connectivity patterns via graph kernels for fmri-based depression diagnostics,” inProc. of IEEE International Conference on Data Mining Workshops(ICDMW), pp. 308–314, 2018. [5] V. Buchstaber and P. T.E., “Toric topology.,”Mathematical Surveys andMonographs. Amer. Mathematical Society, vol. 205, 2015. [6] H. Chen, Q. Dou, L. Yu, J. Qin, and P.-A. Heng, “Voxresnet: Deep voxelwise residual networks for brain segmentation from 3d mr images,”NeuroImage, vol. 170, pp. 446–455, 2018.

Optimal High-Gamma Bandpower Estimation and Denoising for Invasive Brain-Computer Interfaces

Johannes Gruenwald1,2,*, Christoph Kapeller1, Kyousuke Kamada3, Josef Scharinger2, and Christoph Guger1

1g.tec medical engineering GmbH, Schiedlberg, Austria; 2Institute of Computational Perception, Johannes Kepler University, Linz, Austria; 3Hokashin Group Megumino Hospital, Sapporo, Japan.

*E-mail: gruenwald@gtec.at

INTRODUCTION High-gamma activation (HGA) is a task-related bandpower change in brain waves above 50 Hz. It has been linked to cortical processing of sensorimotor, auditory, visual, and memory function [1]–[3]. As a consequence, HGA is a key feature of invasive brain-computer interfaces (BCIs) and the signal-to-noise ratio (SNR) of its estimates determines the overall performance of the BCI. There are different approaches to extract HGA from brain waves: envelope-based (via the Hilbert transform [2], [4]) and bandpower-based [5], [6]. The latter includes time- domain methods, but also time-frequency decompositions.

MATERIALS AND METHODS In this work, we present the optimal method to estimate HGA from brain waves. It is based on time-domain bandpower estimation, including a whitening transform. Since HGA features are noisy, we further propose the optimal solution to denoise the estimates. Our approach is based on adaptive Kalman filtering. The resulting features are given at a rate 50 Hz, which resolves all transients in the physiological signal. We evaluated our methods on synthetically generated ECoG with known ground truth and on real ECoG data from 10 epilepsy patients and 15 recordings of three experiments involving different tasks.

RESULTS We compared our HGA estimation method to the state of the art and observed an average increase of the SNR between +50% and +200%, depending on the experiment. Furthermore, the proposed adaptive Kalman Filter yielded a systematic increase of the SNR between +10% and +60% compared to the standard moving- average filter with optimal window length. In view of the high temporal resolution, the features allow precise HGA onset detection.

CONCLUSION We have proposed two improvements for estimating and denoising HGA for invasive BCIs that significantly outperform the state of the art. Our methods are based on time- domain filtering and are thus applicable for real-time BCIs. We believe that this can substantially improve the decoding performance of future brain-computer interfaces. REFERENCES [1] K. J. Miller et al., “Spectral Changes in Cortical Surface Potentials during Motor Movement,” J. Neurosci., vol. 27, no. 9, pp. 2424–2432, Feb. 2007. [2] C. Kapeller et al., “Real-Time Detection and Discrimination of Visual Perception Using Electrocorticographic Signals,” Journal of Neural Engineering, Jan. 2018.

[3] N. Kunii, K. Kawai, K. Kamada, T. Ota, and N. Saito, “The significance of parahippocampal high gamma activity for memory preservation in surgical treatment of atypical temporal lobe epilepsy,” Epilepsia, vol. 55, no. 10, pp. 1594– 1601, Oct. 2014. [4] W. G. Coon and G. Schalk, “A method to establish the spatiotemporal evolution of task-related cortical activity from electrocorticographic signals in single trials,” Journal of Neuroscience Methods, vol. 271, pp. 76–85, Sep. 2016. [5] A. Sinai, “Electrocorticographic high gamma activity versus electrical cortical stimulation mapping of naming,” Brain, vol. 128, no. 7, pp. 1556–1570, Apr. 2005. [6] J. Gruenwald, C. Kapeller, K. Kamada, J. Scharinger, and C. Guger, “Optimal bandpower estimation and tracking via Kalman filtering for real-time Brain- Computer Interfaces,” in 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), 2017, pp. 605–608.

Cognigraph: a real-time EEG-based source imaging software

Dmitrii Altukhov 1*, Evgenii Kalenkovich 1, Andrey Zhukov2 , Nikolai Smetanin2, Alexei Ossadtchi2

1 Centre for Neuroeconomics and Decision Making, NRU Higher School of Economics, Moscow, Russia 2Centre for Bioelectric Interfaces, NRU Higher School of Economics, Moscow, Russia

E-mail: daltuhov@hse.ru INTRODUCTION Real-time source reconstruction and visualization is one of the current challenges in the field of EEG data analysis. At the same time, modern software packages are primarily focussed on the off-line processing. Our software, dubbed Cognigraph, offers fast and flexible means of real-time EEG source imaging together with 3D rendering of the analysis results.

METHODS Cognigraph is a Python-based GUI application comprising a set of functional blocks assembled together in a data processing pipeline. Incoming data are streamed through in a chunk-by-chunk manner so that the processed chunks can be visualized or used for “brain-state” decoding on the fly.

Cognigraph’s functionality includes:

- Input from files or LSL [link] streams.

- Preprocessing (filtering, downsampling, artefacts rejection)

- Individual MRI-based forward models

- Source estimation (MNE, dSPM, sLORETA, LCMV, MCE)

- Atlas-based ROIs signal extraction

- Connectivity estimation (Coherency, ImCoh, Envelope correlation)

- Animated gif export

RESULTS Cognigraph performs end-to-end source estimation of 128 channel EEG data with subsequent 3D rendering on the grid of 3000 cortical sources at 10 Hz rate or higher. Cognigraph’s module-based architecture supplies flexibility and allows for numerous operation scenarios while the graphical user interface makes it easy to dynamically adjust the data-processing pipeline.

Figure 1 a) Update time histograms of Cognigraph’s inverse solvers, b) - Cognigraph’s GUI

CONCLUSION We’ve built a fast and versatile software tool for real-time EEG source reconstruction and visualization. We believe that in addition to many potential applications Cognigraph will make it easier to exploit the reported accuracy boost [links] for the source space decoding in various BCI designs. REFERENCES [1] https://github.com/labstreaminglayer [2] Peter Lin, Kartikeya Sharma, Tom Holroyd, Harsha Battapady, Ding-Yu Fei,, Ou. Bai (2013). A High Performance MEG Based BCI Using Single Trial Detection of Human Movement Intention, In book: Functional Brain Mapping and the Endeavor To Understand the Working Brain. DOI:10.5772/54550. [3] Edelman BJ, Baxter B, He B. EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks. IEEE Trans Biomed Eng. 2016;63(1):4–14. doi:10.1109/TBME.2015.2467312

Current trends in deep learning for EEG analysis

and how to improve reproducibility of DL-EEG studies

Yannick Roy Faubert Lab, University of Montréal The use of deep learning for EEG data has increased exponentially over the past couple of years and yet it is still hard to understand the best practices. After reviewing 154 scientific papers between 2010 and 2018 across different domains such as sleep, epilepsy, brain-computer interfaces, cognitive and affective monitoring, we've identified the main trends in the field. Over 60 data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. Additionally, we've tried to answer questions such as "how much data is enough data for DL?" and "is deep learning better than traditional machine learning?". Finally, we've come up with some recommendations for researchers in the field.

Considering the classifier variance in Bayesian hyperparameter optimization

Bogdan L. Kozyrsky

NRC Kurchatov Institute, Moscow, Russia E-mail: likan26.09@yandex.ru Keywords: BCI, machine learning, Bayesian methods, hyperparameters In deep learning, optimization of hyperparameters for the Artificial Neural Networks (ANN) is often needed to obtain high performance. Typically, this is done using random search, a time-consuming procedure. An alternative is the use of Bayesian methods that consider the ANN performance as a function of hyperparameters. This function, known as the Acquisition Function (AF), allows to sample hyperparameters optimally [1].

Since brain signal datasets are typically small, the estimates of the classifier performance have large variance; in terms of Bayesian optimization, AF samples have large variance. Furthermore, this variance may itself vary in different regions of hyperparameter space. Gaussian Processes (GP) and Tree Parzen Estimators (TPE) are the most popular functions for SF [2], but they have difficulties dealing with high AF sampling variance. GP approach naturally incorporates sampling variance, but most popular implementations of this algorithm assume that it is constant in all regions of the hyperparameter space. TPE approach has a stronger drawback because it does not take into account sampling variance at all. Overestimating of the pre-set value of sampling variance may lead to slower convergence and degeneration of the Bayesian procedure to Random Search for some regions of hyperparameter space, while its underestimating may lead to the algorithm's overfitting. I propose to solve this problem via blurring borders of regions of hyperparameter subspaces approximations inside TPE algorithm. At the conference, I will present (1) the results of the experimental comparison between presetting sampling variance and step-by-step updating it for GP, and (2) the results of testing the proposed approach on the synthetic data and the data from BCI experiments. ACKNOWLEDGEMENT This work was supported by the Russian Science Foundation, grant 18-19-00593. REFERENCES [1] Rasmussen, C. E. (2003, February). Gaussian processes in machine learning. In Summer School on Machine Learning (pp. 63-71). Springer, Berlin, Heidelberg. [2] Bergstra, J. S., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. In Advances in Neural Information Processing Systems (pp. 2546-2554).

Is subcortical fMRI only machine learning away from scalp EEG?

I. Mikheev1*, V. Zemlyak1, I. Dybushkin2, A. Lebedev1 and A. Ossadtchi1

1 National Research University Higher School of Economics, Moscow

2 Moscow State University, Moscow

*E-mail: instantofeternity@gmail.com

INTRODUCTION Simultaneous EEG-fMRI recordings combined with artifact rejection methodology and machine learning techniques can be used to test the hypothesis about our ability to predict activity of subcortical structures from EEG alone.

MATERIALS AND METHODS Simultaneous EEG-fMRI data is affected by gradient, ballistocardiographic (BCG) artifacts and the interference from external equipment such as helium pump. In this study we used simultaneous resting state EEG-fMRI recordings from 5 participants in helium pump on and

off conditions [1]. We extracted BOLD activity in 21 subcortical regions using Harvard-Oxford subcortical atlas. To predict BOLD signal measured during acquisition of a particular fMRI volume we used feature space formed by the absolute values of the Short Time Fourier Transform of 16 seconds of EEG data immediately preceding this volume. We used ridge regression and CNN [2] to build EEG->fMRI predictor. To validate our results, we distorted causal EEG-fMRI relationship by time-reversing EEG signal w.r.t. fMRI data and trained a new decoder on this surrogate dataset.

RESULTS As illustrated in Fig. 1 A - BOLD signal in most subcortical structures can be decoded from EEG with surprisingly high accuracy in the helium pump off condition; B, C - CNN offers a tangible improvement in predicting BOLD signals in the majority of subcortical structures. D - as expected, time-reversing EEG data breaks causal relationship and does not allow us to obtain an efficient EEG->fMRI predictor.

Figure 1. Performance of the EEG-fMRI predictor

CONCLUSION Our findings extend the emerging literature on EEG-fMRI relationship [3] and show that it is possible to predict BOLD signal of subcortical regions for each individual subject using the concurrently acquired EEG data.

ACKNOWLEDGEMENT We thank Dr. Johan van der Meer for providing EEG-fMRI dataset [1].

REFERENCES [1] van der Meer, J., Pampel, A., van Someren, E., Ramautar, J., van der Werf, Y., Gomez-Herrero, G., ... Walter, M. (2016). "Eyes Open - Eyes Closed" EEG/fMRI data set including dedicated "Carbon Wire Loop" motion detection channels. Data in brief, 7, 990–994. doi:10.1016/j.dib.2016.03.001 [2] Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., ... & Kavukcuoglu, K. (2016). WaveNet: A generative model for raw audio. SSW, 125. [3] Meir-Hasson, Y., Kinreich, S., Podlipsky, I., Hendler, T., & Intrator, N. (2014). An EEG finger-print of fMRI deep regional activation. Neuroimage, 102, 128-141.

MNEflow: open-source academic software for applying neural networks to electromagnetic brain measurements and brain-computer interfacing

Ivan Zubarev

Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland

INTRODUCTION Neural Networks (NNs) are becoming an increasingly popular tool for classification and automated analysis of EEG and MEG data. Our recent work introduced two convolutional neural network models that outperform benchmark methods in multiclass classification and allow interpretation of the MEG signal components that inform the classification [1]. In this presentation, we will briefly discuss these methods and present MNEflow an open-source software implementing these and other popular NN models.

AIMS The purpose of MNEflow is two-fold: first, to provide a convenient tool for researchers who would like to apply neural network models to non-invasive brain measurements. To this end, we demonstrate how the data processing pipeline can be optimized for model development and typical experimentation. Second, to provide an API for developing novel DNN architectures for classification of event-related and continuous (e.g. movement-related) brain states.

DESIGN AND RESULTS We demonstrate the proposed data processing pipeline and API in the context of the two studies involving 1) offline multilabel classification of the sensory evoked responses and 2) offline continuous decoding of the movement-related brain states in a motor task.

CONCLUSION MNEflow provides a high-level API allowing researchers to apply and develop neural network models optimized for performing classification and interpretation of EEG and MEG data. We also briefly discuss future directions including the application of NNs to single-trial regression problems and real-time applications in brain-computer interfaces. The implementation and API reference are freely available online at https://mneflow.readthedocs.io

REFERENCES [1] Zubarev I, Zetter R, Halme H, Parkkonen L, (2019) Adaptive neural network classifier for decoding MEG signals. NeuroImage vol: 197 pp: 425-434

Poster session

The Effect of Feedback Signal Presentation Latency on the Effectiveness of Training in Neurofeedback Paradigm

Anastasiia Belinskaia , Nikolai Smetanin , Alexei Ossadtchi

Centre for Bioelectric Interfaces, National Research University Higher School of Economics, Moscow E-mail: belinskaya.anastasy@gmail.com Keywords: Neurofeedback, Real-time EEG INTRODUCTION Although the first neurofeedback (NFB) experiments date back nearly six decades ago, it is still controversial whether NFB is an efficient method for brain activity tuning. Among various NFB paradigm characteristics, feedback latency is an important parameter that may significantly affect learning [1][2].

METHODS To investigate the effect of latency we trained four groups of subjects (10 participants in each group) to upregulate their occipital alpha-rhythm power (p4 channel) and used three different artificially imposed additional feedback latency values (0, 250 ms and 750 ms) and mock feedback.

RESULTS All three feedback groups demonstrated a steady and significant growth of alpha band power as compared to the mock group. We examined the patterns of alpha-bursts in more detail (the occurrence rate of bursts, duration, and amplitude). For low latency, subjects were able to increase the occurrence rate of alpha bursts but not their amplitude or duration, which agrees with our previous findings [3]. However, for the greater latency, changes with respect to the mock group were observed in all characteristics of the alpha rhythm.

CONCLUSION We conclude that variation of feedback signal latency even within the range of large values typical to commercial NFB systems differential affects occipital alpha rhythm morphology. Our findings suggest that NFB latency is an extra parameter that could be manipulated to achieve desired changes in the fine characteristics of EEG activity. Exploration of lower latency values is of particular interest but requires a significant technological advance REFERENCES [1] Skinner, B. F. (1958). Reinforcement today. American Psychologist, 13, 94–99. [2] Smetanin, N., Lebedev, M., & Ossadtchi, A. (2018). Towards zero-latency neurofeedback. bioRxiv, 424846. [3] Ossadtchi A., Shamaeva T., Okorokova E., Moiseeva V., and Lebedev M. Neurofeedback learning modifies the incidence rate of alpha spindles, but not their duration and amplitude. Scientific Reports, 2017.

ECOG based inverse modelling for decoding and eloquent cortex mapping

Valentina Bulgakova*, Mikhail Lebedev, Alexei Ossadtchi

Centre for Bioelectric Interfaces, Higher School of Economics, Moscow *E-mail:bulgakovavvalentina@gmail.com

INTRODUCTION Solving the ECoG inverse problem reconstructs neural source activity [1], which may improve decoding in neural prostheses and can be used for accurate delineation of eloquent cortex in neurosurgical planning.

METHODS ECoG data was recorded from 8x8 ECoG grid located over the left sensorimotor cortex of a patient with epilepsy conducting flexion-extension motions of right-hand digits for 1 minute. Digit trajectories were by Perception Neuron [2]. Anatomical model was based on structural MRI and CT. Lead-field matrix was computed with OpenMEEG [3]. Five approaches of solving the inverse problem were implemented in MATLAB [4] : MNE, wMNE, sLORETA, eLORETA, and LCMV beamformer [5,6,7]. Decoding was done using instantaneous power values in 8 narrow bands of the sensor or source-reconstructed ECoG data. Data was divided into training and test sets, Pearson correlation coefficient between the actual and predicted signal of the test segment was used as a measure of performance, computed for each sensor and each cortical vertex in the source space.

RESULTS Results of the maximal decoding accuracies for each sensor or vertex, each method and digit are displayed in Table 1. Figure 1 shows the decoding accuracy for every source, the red point corresponds to the source location with the maximal accuracy.

Table 1 . Highest digit decoding accuracy.

Figure 1. Decoding accuracy for source-reconstructed signals.

CONCLUSION Our results extend the literature on non-invasive interfaces where decoding based on the inverse solution improved classification of motor imagery states [8,9,10]. Here we show that ECOG based inverse solvers meaningfully improve decoding of continuous trajectories from neural signals. This approach may also improve passive cortical mapping accuracy.

REFERENCES [1] Zhang, Yingchun, et al. "Three-dimensional brain current source econstruction from intracranial ECoG recordings." Neuroimage 42.2 (2008): 683-695. [2] “Perception Neuron by Noitom.” Perception Neuron by Noitom, neuronmocap.com/ [3] Kybic, Jan, et al. "A common formalism for the integral formulations of the forward EEG problem." IEEE transactions on medical imaging 24.1 (2005): 12-28. [4] MATLAB. (2018). version 9.3.0,681905 . Natick, Massachusetts: The MathWorks Inc. [5] Hämäläinen, Matti, et al. "Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain." Reviews of modern Physics 65.2 (1993): 413. [6] Pascual-Marqui, Roberto D. "Discrete, 3D distributed, linear imaging methods of electric

neuronal activity. Part 1: exact, zero error localization." arXiv preprint arXiv:0710.3341 (2007). [7] Van Veen, Barry D., et al. "Localization of brain electrical activity via linearly constrained minimum variance spatial filtering." IEEE Transactions on biomedical engineering44.9

(1997): 867-880. [8] Qin, L., Ding, L., and He, B. (2004). Motor imagery classification by means of source analysis for brain-computer interface applications. Journal of Neural Engineering, 1(3):135–141. [9] Kamousi, B., Amini, A. N., and He, B. (2007). Classification of motor imagery by means of cortical current density estimation and von neumann entropy. Journal of Neural Engineering, 4(2):17. [10] Edelman, B. J., Baxter, B., and He, B. (2016). EEG source imaging enhances the decoding of complex right-hand motor imagery tasks. IEEE Transactions on Biomedical Engineering, 63(1):4–14.

Development of a hardware-software system for performance testing with non-invasive control of the lactate threshold

D. A. Buyanov

Medical Computer Systems Ltd., Moscow, Zelenograd, Russia; National Research University of Electronic Technology (MIET), Moscow, Zelenograd, Russia

E-mail: Buyancik@gmail.com Keywords: NIRS, EMG, lactate threshold

When exercise is high, muscles work without oxygen. They burn adenosine triphosphate using glucose and glycogen for fuel, and lactic acid (lactate) is released as a by-product. The lactate threshold is the border where a balance is reached between the rate of lactate release by the muscles and the rate of its utilization. The limit corresponds to the individual level of intensity of physical activity and pulse. Exercise in the anaerobic zone, when done correctly and safely, results in higher muscular endurance. The purpose of the work is to develop a hadware-software system for non-invasive determination of lactate threshold. Existing analogues do not fully meet the needs of the user. As a rule, the lactate threshold is determined during the test with a linearly increasing load, during which blood samples, mainly arterial, are repeatedly taken to determine the concentration of lactate. The method is accompanied by loss of time for blood sampling and cannot be used in real time. The developed system allows determining the lactate threshold by a point on the smoothed curve reflecting the dynamics of the EMG activity intensity during the test, which corresponds to the position of the inflection point on the graph of the dependence of the average value of the deoxygenated hemoglobin in muscle tissue measured by IR - spectroscopy, from the average value of its EMG activity. A prototype was made that implements the hardware and software of the device. Functional tests confirmed the feasibility of the technical requirements. Based on the tests formed a number of further improvements. The tests proved the viability of the proposed method. That will allow, in the future, to measure the lactate threshold non-invasively in real time with people involved in sport.

Patient’s motion capture when performing UPDRS for an objective diagnosis of motor disorders in Parkinson disease

Kirill A. Fadeev1, Alex V. Tumialis*1, Kirill S. Golokhvast1, Artur R. Biktimirov2

1 Laboratory of educational psychophysiology, Far Eastern State University, Vladivostok, Russia 2 Medical Centre, Far Eastern State University, Vladivostok, Russia * E-mail: tumialis.av@dvfu.ru Keywords: Parkinson disease, brain stimulation, motion capture INTRODUCTION The traditional method of Parkinson's disease diagnostic is the subjective assessment of the patient’s motor functions impairment using standardized clinical scales. The use of modern method of motion tracking will bring the diagnosis Parkinson's disease to a qualitatively higher level of objective assessment of the patient's condition.

MATERIALS AND METHODS The study involved 8 patients with Parkinson's disease (age=61.5 years, duration of disease=10.4 years, dose of L-DOPA=964, UPDRS off=44, UPDRS on=13.2, UPDRS stimulator=10.4; number: men=5, tremor=5, akinetic rigid syndrome=5, dyskinesia=6). The study was carried out within the standard clinical diagnostics of motor disorders of part 3 UPDRS in the ON and OFF conditions of L-DOPA and after implantation and activation of the brain stimulator using the Perception Neuron motion-capture suit. The angle of rotation of the hand when performing the test by the wavelet analysis method was analyzed.

RESULTS The statistical difference between patient’s conditions obtained from the motion tracking suit was similar to the subjective assessment of the patient's condition by the clinician. In particular, in the condition of L-DOPA ON, a higher frequency of hand rotation was found as compared with the condition of L-DOPA OFF. In the condition with the stimulator turned on the angle of rotation was greater compared with the condition of L-DOPA ON and OFF and the hand rotation frequency was higher than in the condition of L-DOPA OFF.

CONCLUSION The obtained data confirm the hypothesis about the possibility of objectification and standardization of the analysis of the patient’s condition and the dynamics of progression of motor manifestations of Parkinson's disease and also can be used to create diagnostic platform.

Neurofeedback for correction of stressinduced states

A.I. Fedotchev1 , S.A.Polevaia2* , S.B. Parin3

1 Institute of Cell Biophysics, Russian Academy of Sciences, Moscow Region; 2 Privolzhskiy Research Medical University, Nizhny Novgorod; 3 Lobachevsky State University of Nizhny Novgorod

E-mail: sofiapolevaia@gmail.com Key words: neurointerface; electroencephalogram (EEG); EEG transformation into music-like signals; photostimulation, controlled by brain biopotentials; correction of functional disorders. INTRODUCTION The aim of the study was to test this hypothesis experimentally by comparing the effects observed when stress-induced states are suppressed by means of neurointerface using single (sound) or double (light- sound) feedback from the EEG or listening to music. MATERIALS AND METHODS The study involved 14 subjects aged 18 to 60 years. In one of three experiments were presented with classical music (control). In the other two experiments, either a single feedback was used, in which subjects are presented with sound stimuli obtained by converting the current values of EEG oscillators into music-like signals, or a double feedback, in which the described music-like signals were supplemented by rhythmic light stimuli controlled by the raw EEG of the subject. RESULTS The data demonstrate that significant changes in objective and subjective indices are observed only in the presence of feedback from the EEG, i.e. in cases where the management of sensory stimulation is carried out directly by the subject’s EEG. In these cases, a significant increase in alpha EEG power relative to the background is noted, accompanied by positive emotional reactions and shifts in the functional state of the subjects. CONCLUSION Doubling the feedback from subject’s EEG seems to be a promising way to improve the effectiveness of neurofeedback procedures for correcting stress-induced functional disorders. With such treatments, optimal conditions are created for involving the integrative, adaptive and resonance mechanisms of the central nervous system in the processes of normalization of organism functional state. The work was supported by the Russian Foundation for Basic Research: grants No.18-013-01225, 18-413-520006, 19-013-00095).

NeuroChat approbation results

Natalia Galkina1* , Alexander Luzhin1 , Alexander Kaplan2 1 NeuroTrend, 2 Lomonosov Moscow State University

* E-mail: galkina@neurotrend.ru Keywords: BCI, communication, P300

The NeuroChat (NC) hardware-software complex is a communication device developed for people with severe speech and movement disorders. This complex consists of a neuro-headset and specialized software and allows communication via the Internet. The product’s novelty lies in the user’s ability to establish a two-way, as well as network

communication between persons who are unable to control their motor activity and therefore cannot speak or type. This communication solution is based on the P300 ERP component and a BCI paradigm. Currently NC is being used, to varying degrees of intensity, in over 60 medical institutions in 40 regions of Russia, in addition to over 200 patients who are using the complex at home. We have analyzed the data from 149 representative patients. These users were trained by the NC patronage service that had been created specifically for this purpose. Each user underwent 5-11 training sessions. Analysis of the complex’s usage effectiveness suggests the following: Patients require 4-6 training sessions to master the typing technique enabled by NC. Following these sessions typing speed continues to increase at a slower rate, but cognitive load (and therefore, tiredness) while using this technology decreases. During the first few sessions, the average typing speed is about 60 seconds per letter. Following training completion, it increases to 40 seconds per letter. A healthy experienced user takes just 10-12 seconds to type a letter. We have observed that highly motivated users take significantly less time and effort to master this technology. NC has shown to be highly effective in allowing communication for persons with severe speech and movement disorders, although learning to use the complex is difficult without the help of a specialist.

EEG signal analysis from the standpoint of the structural approach

Yakov Furman1, Viktor Sevastyanov1, 2, Konstantin Ivanov1,* 1Volga State University of Technology, Yoshkar-Ola. Russia;

2Center of Speech Pathology and Neuro Rehabilitation, Yoshkar-Ola. Russia

*E-mail: krtmbs@volgatech.net

Keywords: EEG data analysis, EEG segmentation, machine learning

INTRODUCTION The need to increase the reliability of EEG-based clinical conclusions highlights the importance of developing algorithms for EEG automatic classification to allow EEG data presentation in a readily comprehensible form and facilitate clinical decision-making [1].

MATERIAL AND METHODS To solve the problem of EEG automatic classification, the syntactic approach may be used in which a pattern to be recognized (EEG) is constructed by linking together simple subpatterns. We used EEG (wave) segments bounded by global and local minimum points as subpatterns [2]. To implement the structural approach, we developed an EEG segmentation algorithm allowing representation of a signal as an ordered sequence of segments. We developed algorithms for EEG segment classification performed by determining informative features of segment shapes; the features are used in EEG visual assessment. To obtain the quantitative characteristics of EEG segment shapes, a new contour model of EEG was proposed [3].

RESULTS Software implementation of the algorithms forms a clinical conclusion containing the outcomes of EEG segment classification. Analysis of approximately 600 EEG epochs from

the PhisioNet and TUH EEG databases has revealed that the use of such supplementary diagnostic data by the neurologist can increase the probability estimates of correct clinical conclusions from 0.86 to 0.92 for normal EEG, from 0.81 to 0.88 for abnormal EEG, and from 0.9 to 0.98 for pathological EEG [4].

CONCLUSION According to our findings, the use of the proposed approach can increase the reliability of a clinical conclusion made by the neurologist. Further research will focus on developing structural methods for EEG analysis to perform EEG classification based on relative positioning of classified patterns.

REFERENCES [1] Juri D. Kropotov. Quantitative EEG, Event-Related Potentials and Neurotherapy (Academic Press, New York, London, 2009). [2] Ya. A. Furman, V. V. Sevastyanov, and K. O. Ivanov, “Modern problems of brain-signal analysis and approaches to their solution,” Pattern Recogn. Image Anal. 29 (1), 99–119 (2019). [3] Ya. A. Furman, V. V. Sevastyanov, and K. O. Ivanov, “Contour analysis of a fine structure in an electroencephalogram,” Pattern Recogn. Image Anal. 26 (4), 758–772 (2016). [4] Ya. A. Furman, V. V. Sevastyanov, and K. O. Ivanov, “Analysis and classification of EEG elements based on the contour model”. In BIOMEDSYSTEMS-XXXI All-Russian Scientific and Technical Conference: Conference proceedings. Ryazan, 2018. - Pp. 198–204 [in Russian].

Somatotopy of Excitation and Inhibition Probed by pp TMS – preliminary results

Ekaterina Ivanina1, Anastasia Asmolova1, Michael Ivanov1, Novikov Pavel2, Vadim Nikulin2,3,5, Maria Nazarova2,6*

1

- Department of psychology, Higher School of Economics, Moscow, Russia, 2 - Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia, 3 - Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 4 - Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Berlin, Germany, 5 - Bernstein Center for Computational Neuroscience, Berlin, Germany, 6 - Federal State Budget Institution Federal Center for Cerebrovascular Pathology and Stroke of the Ministry of Healthcare of the Russian Federation

*E-mail: chantante@gmail.com

Paired pulse transcranial magnetic stimulation (ppTMS) allows probing cortico-cortical excitatory and inhibitory circuits involving different neurotransmitter systems [1]. PpTMS phenomena are usually classified by the length of the interstimulus interval (ISI) and relate to local and widespread trans-synaptic interactions [2]. However, currently, it is not known how ppTMS phenomena are expressed for different muscles and whether they depend on an inter-pulse interval (IPI). In this study, the aim was to investigate these topics by measuring

simultaneously several muscles. 12 healthy young volunteers were enrolled (18-21 y.o.) Four ppTMS phenomena (SICI/LICI – short/long-interval intracortical inhibition (2/100 ms), SICF – short interval intracortical facilitation (3 ms), ICF – intracortical facilitation (2 ms)) were probed. Motor evoked potentials from four right upper limb muscles - abductor pollicis brevis, extensor digitorum communis, abductor digiti minimi and biceps brachii were registered. Data analysis was done in Matlab and SPSS Software. On average the strength of ppTMS phenomena was lower than 1 for inhibitory phenomena: SICI - 0.94, LICI - 0.37 and higher than 1 for facilitatory: SICF - 1.686, ICF - 2.999. The strength of SICI, ICF and LICI phenomena correlated significantly between muscles (0.831, 0.759, 0.685, p<0.05 Spearman's CC, correspondingly), while no significant correlation among muscles was found for SICF. Our preliminary results demonstrate that there is no link between IPI and inter-muscle correlations of ppTMS phenomena. SICF which is possibly mediated by I-wave facilitation is the only highly local phenomenon among the investigated ones for which no correlation even for nearby located hand muscles was shown. This in turn provides further information about the spatial dissociation of neuronal circuitry involved in different ppTMS phenomena. REFERENCES [1] Ziemann U. et al. TMS and drugs revisited 2014 // Clin. Neurophysiol. International Federation of Clinical Neurophysiology, 2015. Vol. 126, № 10. P. 1847–1868. [2] Menon P., Kiernan M.C., Vucic S. Cortical excitability varies across different muscles // J. Neurophysiol. 2018. Vol. 120, № 3. P. 1397–1403.

EEG classification of motor imagery using convolutional neural network

Maria Kim1 , Victoria Panchenko1* , Maria Sergeeva1 , Vladimir Bulanov2 , VasilyPyatin1 1 Samara State Medical University (Samara, Russia) 2 IT Universe Ltd (Samara, Russia) *E-mail: viktoryest@gmail.com INTRODUCTION Nonlinear classifiers and specifically convolutional neural networks (CNN) are widely used to detect and classify the EEG patterns for different BCI applications because they allow to combine extraction and classification of features, and to simplify processing of raw EEG data [1] [2]. In this study we apply CNN to upper limbs motor imagery (MI) classification.

MATERIALS AND METHODS The study involved 10 healthy subjects aged 21–22 years, all females, right driven, BCI naive, given a visual stimulation to perform MI tasks: clenching a fist and flexing an elbow, alternating with rest state. Each subject performed 10 sessions consisted of two consecutive 11 minutes trials with a 5 minute pause. 22 channels EEG data were recorded with an NVX36 (MCS, Russia) amplifier and mb.EEG software (IT Universe Ltd). We have developed the classifier using Braindecode Shallow ConvNet CNN toolbox [3]. Data preprocessing included filtering, artifacts removal, segmentation and standardization. The training, validation

and test datasets were formed in sequential paradigm: the first part of each session data was included in a training dataset, the second part - in a validation set, and the last one - in a test dataset. Thus we simulated the real use case when a classifier is initially trained, then validated, and then applied to an online data flow analysis.

RESULTS The final result of processing of all 100 records will be presented in our report at the conference. By the moment of abstract submission we have reached an average 78.9 ±10.3% [63.2 - 93.3] accuracy of detection of the left arm vs. right arm vs. rest state motor imagery.

CONCLUSION CNN are promising in motor imagery BCI, have a number of advantages and allow to achieve high accuracy of multiclass features detection, which enables their further use in real-time BCI.

REFERENCES [1] Craik A. et al. Deep learning for Electroencephalogram (EEG) classification tasks: A review Journal of Neural Engineering 2019 doi:10.1088/1741-2552/ab0ab5 [2] Das S., Tripathy D., Raheja J.L. (2019) A Review on Algorithms for EEG-Based BCIs. In: Real-Time BCI System Design to Control Arduino Based Speed Controllable Robot Using EEG. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore https://doi.org/10.1007/978-981-13-3098-8_3 [3] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D., Glasstetter, M. , Eggensperger, K., Tangermann, M. , Hutter, F. , Burgard, W. and Ball, T. (2017), Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp., 38: 5391-5420. doi:10.1002/hbm.23730

Neurophysiological mechanisms of emotional-cognitive interaction with BCI - P300

Luiza Kirasirova*, Vasiliy Pyatin

Samara State Medical University *E-mail: lkirasirova063@gmail.com INTRODUCTION Brain computer interfaces (BCI) can serve as a neurocommunication system for people with severe impairment in speech and motor function [1]. Reasons for differences in capability of BCI usage haven’t been completely studied yet. Heart rate variability (HRV) is considered to be an indicator of the complex interaction between the cardiovascular system and the brain [2]. According to neurovisceral integration model vagal heart tone may reflect the functional balance of neural networks that are involved in emotional and cognitive interactions. HRV during neurocommunication has not been completely studied and understood yet. The purpose of this study was to investigate the heart rate variability of a person during neurocommunication by means of the P300-based Brain-Computer Interface.

MATERIALS AND METHODS The study involved 18 male volunteers aged 18 – 20. All subjects signed informed consent to participate in the study. None of the volunteers suffered from cardiovascular diseases, and was not in the acute period of other diseases at the time of the study. P300-based Brain-Computer Interface is composed of the 8-channel electroencephalograph GarAnt-EEG and a program in the form of a speller with the cells of cyrillic alphabet letters, punctuation marks and numbers. The registration of HRV was performed by using the Eloks-01 pulsoximeter and the Elograf 3.0 program. The study session design included three stages each lasting 5 minutes of signal registration. At the first stage we recorded the baseline state of the HRV, at the second stage we recorded the same process but with the use of the program which presented the target stimulus (calibration), at the final stage the participant chose this target stimulus himself and has to type the 13-letters-word , the same for every participant. Measures were then evaluated by the Wilcoxon signed-rank test in the Statistica 12 software. The parameters of quasi-attractors (QA) were estimated by the method of multidimensional phase spaces [3]. Systematization of data was carried out by means of Microsoft Office Excel 2016.

RESULTS Statistically significant differences were found between the first and the third stages of the study. They included the decrease in HF (p =0.004), the increase in p (LF) (p <0.001), the decrease in p (HF) (p <0.001), the increase in LF / HF (p <0.001). The differences in the form of the increase in p (LF) (p = 0.021) and p (HF) (p = 0.021) were also revealed between the first and the second stage. The method of multidimensional phase spaces revealed statistically significant differences between the stochastic center of a 3D QA formed by three-dimensional phase space (SDNN, dNN/dT) at the third stage of the research as compared with the initial state (p = 0.018) and the calibration stage (p = 0.042).

CONCLUSION Our findings show that while using BCI system the participation of neural networks of the brain in the emotional-cognitive interaction during neurocommunication demonstrates similar level at the stage of calibration and during the spelling of the reference word. This occurs in increased sympathetic activity and decreased parasympathetic nervous regulation. ACKNOWLEDGMENTS: The reported study was funded by RFBR, project number 19-315-90120 REFERENCES [1] Kaufmann T1, Vögele C, Sütterlin S, Lukito S, Kübler A. Effects of resting heart rate variability on performance in the P300 brain-computer interface. International journal of psychophysiology: official journal of the International Organization of Psychophysiology. 2011;83(3):336-41 DOI: 10.1016/j.ijpsycho.2011.11.018. PMID: 22172335 [2] Gernot Ernst. Heart-Rate Variability – More than Heart Beats? Frontiers in Public Health. 2017; 5: 240. DOI: 10.3389/fpubh.2017.00240. PMID:28955705 [3] Eskov V.M., Braginskiy M.Y., Rusak S.N., Ustimenko A.A., Dobrynin U.V. Programma identifi katsii parametrov attraktorov povedeniya vektora sostoyaniya biosistem v m-mernom fazovom prostranstve. The certificate on official registration of the program on the computer No 2006613212 From September, 13th 2006 ROSPATENT Moscow 2006.

MEG based functional microscopy using traveling wave priors: a new technology for exploring epilepsy

Aleksandra Kuznetsova*, Alexei Ossadtchi

Centre for Bioelectric Interfaces, NRU Higher School of Economics, Moscow, Russia *E-mail: kuznesashka@gmail.com

INTRODUCTION The major goal of presurgical diagnostics for patients with medically intractable multifocal epilepsy is the localization of seizure onset zone (SOZ). Currently only 70% of patients become seizure-free after the surgery, which is partly explained by inaccurate SOZ localization [3]. Novel approaches for detailed analysis of interictal recordings are needed in order to improve the diagnosis accuracy. In this work we hypothesize that cortical activity underlying interictal spikes behaves like a traveling wave [1, 4]. We have developed a novel method that allows us to identify traveling wave parameters that pertain to individual spikes and applied it to the MEG data recorded from epileptic patients.

MATERIALS AND METHODS We have employed ASPIRE approach [2] to obtain a finite set of spatial clusters corresponding to irritative zones. Then we describe interictal spike neural activity as a linear combination of precomputed basis traveling waves generated from one starting point in the ROI and propagating with different speeds from 0.3 m/s to 1.5 m/s in six directions along the cortical surface. We employ the LASSO technique [5] with positively constrained coefficients to find a minimal combination of such basis waves sufficient to describe the MEG spatial-temporal pattern of each interictal spike.

RESULTS We applied the proposed methodology to interictal MEG recordings from three patients. We found that the epileptic clusters significantly differ by the percentage of spikes that can be sufficiently well described with the traveling wave model using a small number of dominant propagation directions. Intriguingly, clusters with the largest proportion of such wave-like spikes appear to coincide with brain regions removed during the surgery that had Engel I outcome in these patients.

REFERENCES [1] Martinet L., Fiddyment G., Madsen J., Eskandar E., Truccolo W., Eden U., Cash S., Kramer M. (2017), Human seizures couple across spatial scales through travelling wave dynamics, Nature Communications, pp. 1-13. [2] Ossadtchi A., Baillet S., Mosher J.C., Thyerlei D., Sutherling W., Leahy R.M. (2003), Automated Interictal Spike Detection and Source Localization in MEG using ICA and Spatio-Temporal Clustering, Clinical Neurophysiology, vol. 115, no. 3, pp. 508-522. [3] Schuele S., Lüders H. (2008), Intractable epilepsy: management and therapeutic alternatives, Lancet Neurol, vol. 7, pp. 514-624.

[4] Tomlinson S., Bermudez C., Conley C., Brown M., Porter B., Marsh E. (2016), Spatiotemporal Mapping of Interictal Spike Propagation: a novel methodology applied to pediatric intracranial EEG recordings, Frontiers in Neurology, vol. 7, no. 229, pp. 1-12. [5] Tibshirani, R. (1996), Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society, vol. 58, no. 1, pp. 267-288.

Modular artificial neural network for neuroscience education

Sergey Kravchenko* Kuban State Medical University, Krasnodar, Russia * Email: ksv.1991@yandex.ru INTRODUCTION Neuroscience is important field for brain-computer interface (BCI) developing. Neural circuits modelling can improve the neurophysiological education. The aim of this research is developing a prototype of hardware artificial neural network for modeling biological neural circuits in education process.

MATERIALS AND METHODS The leaky integrate-and-fire neuron model [1] was used in this work. Each neuron was implemented as single-board module with ARM microcontroller with firmware emulates neuron membrane potential dynamics. One 16-bit port’s pin of microcontroller was configured as output and used as axon. Another port was configured as input and used as dendrites. All modules work with another board, which provides power supply and controls their work. Axons of each neuron were connected to other neuron’s dendrites by wires. Each module has LED spike indicator.

RESULTS Cluster of 8 neurons demonstrates some features, which can be observed in living neural circuits. Any neuron demonstrates spatial and temporal summation and plasticity. Neurons can be connected as chains, loops and other circuits.

CONCLUSION Developed system provides good abilities for modelling neural circuits in neuroscience studying. In comprising with hardware analog [2] its can be used for large scale networks modelling, because each neuron has 16 inputs.

REFERENCES [1] Anokhin K.V, Burtsev S.M., Ilyin V.A, et al. “A review of computational models of neuronal cultures in vitro”, Mat. Biolog. Bioinform., 7:2 (2012), 372–397 [2] Petto A, Fredin Z, Burdo J. The Use of Modular, Electronic Neuron Simulators for Neural Circuit Construction Produces Learning Gains in an Undergraduate Anatomy and Physiology Course. // Journal of Undergraduate Neuroscience Education. 2017; 15(2):A151-A156.

The use of postural balance for the rehabilitation of children with ADHD

Uliana Nasonova 1, Valeria Katunova1*, Anastasia Sukhanova2

1 Privolzhsky Research Medical University MOH Russia, Nizhnij Novgorod, Russia 2 Lobachevsky State University of Nizhny Novgorod, Nizhnij Novgorod, Russia *E-mail: katunova@mail.ru Keywords: postural balance, postural control, biofeedback, ADHD. INTRODUCTION Attention deficit hyperactivity disorder (ADHD) is a common disorder and occurs in 6-12% of children from 4-6 years. Patients with ADHD are often inattentive, impulsive, and hyperactive. They have impaired motor functions and reduced postural control in addition to mental [1, 2]. The purpose of the study is to identify the relationship of motor and mental functions in children with ADHD 7–8 years old.

MATERIALS AND METHODS The pilot study involved 19 children aged 7–8 years. Experimental group – 11 children with clinical attention disorder (ADHD); control group – 8 children without diagnosis. Diagnosis of attention disorders by the Toulouse-Pieron test (level of concentration and the stability of attention). Diagnosis of other disorders was carried out by the neuropsychological method. Evaluation of postural balance was carried out using the postural balance complex of biofeedback (MERA, ST-150). Diagnosed equilibrium, balance control, movement coordination [3]. Statistical processing: parametric criteria, correlation analysis.

RESULTS A group of children with ADHD showed significant differences from the control group on the effectiveness of balance management and coordination of movements. The success of maintaining balance in groups at the same time has the same level. Correlation analysis revealed significant relationships between 1) maintaining an equilibrium state and the speed of switching attention (-0.50), impaired visual and auditory gnosis (0.46); 2) the effectiveness of managing balance and memory disorders (-0.31), emotional disorders (-0.30); 3) the success of motor coordination and acoustic disturbances (-0.36), emotional disturbances (-0.34).

CONCLUSION Diagnosing the effectiveness of managing balance and coordination is important when assessing the severity of ADHD. Motor training aimed at the formation of motor skills can improve motor coordination capabilities and mental functions of the child. Training in postural resistance, proposed for children as a correction of clinical manifestations, may be part of the combined treatment of ADHD.

REFERENCES [1] Ren, Y., Yu, L., Yang, L., Cheng, J., Feng, L., Wang, Y. (2014). Postural control and sensory information integration abilities of boys with two subtypes of attention deficit hyperactivity disorder: a case-control study // Chinese medical journal. – Vol. 127, is. 24. – Pp. 4197-203.

[2] Goetz, M., Schwabova, J.P., Hlavka, Z., Ptacek, R., Surman, C.B. (2017). Dynamic balance in children with attention-deficit hyperactivity disorder and its relationship with cognitive functions and cerebellum // Neuropsychiatric Disease and Treatment. – Vol. 13. – Рр. 873-880. doi: 10.2147/NDT.S125169. eCollection 2017. [3] Aydinli, F.E., Çak, T., Kirazli, M.Ç., Çinar, B.Ç., Pektaş, А., Çengel, E.K., Aksoy, S. (2018). Effects of distractors on upright balance performance in school-aged children with attention deficit hyperactivity disorder, preliminary studyEfeitos de distrações sobre o desempenho do equilíbrio vertical em crianças em idade escolar com transtorno de déficit de atenção e hiperatividade – estudo preliminar // Brazilian Journal of Otorhinolaryngology. – Vol. 84, is. 3. – Pp. 280-289. doi: 10.1016/j.bjorl.2016.10.007.

Changes in the spectral power of EEG in right-handers and left-handers in the process of legs movements imagination

Morenova K.A.*, Vedyasova O.A.

Samara National Research University, Samara, Russia E-mail: morenova_ks@mail.ru

INTRODUCTION Analysis of EEG-correlates of human motor activity is important for solving actual problems of neurophysiology such as the brain-computer interface and functional hemispheric asymmetry. The aim of the work is to study the EEG changes in individuals with different profiles of motor domination during imaginary motor acts of the right and left legs. MATERIALS AND METHODS 55 right-handers, 26 left-handers were examined. EEG was registered with according to the “10–20” international scheme at rest and during imagination of flexion and extension of right and left foot. Evaluated the spectral power of the rhythms of standard frequency ranges in right and left symmetrical leads. RESULTS The imaginary movements of right and left foot reduced the spectral power of EEG in all subjects. Between right-handers and left-handers in paired EEG leads there were differences in the level of rhythms depression, especially alpha and beta1. In right-handers, the change in the power of these rhythms in symmetrical cortical zones depended on an imaginary use of leading and non-leading legs and prevailed in contralateral hemisphere. In left-handers, the degree of decrease the spectral power of alpha and beta1 waves in paired frontal, central, temporal and occipital leads during imaginary actions of leading and non-leading leg did not differ, whereas in the parietal area in both cases the right-side effects dominated. CONCLUSION The obtained data testify to the peculiarities of interhemispheric dynamics of EEG rhythms during the presentation of foot movements in right-handers and left-handers, including a lesser degree of integration of the cortical zones of the contralateral hemisphere into planning and implementation mechanisms of right and left leg movements in left-handers.

EMG based finger bending tracking for VR applications

Vitaliy Petrov*, Stepan Botman, Viktor Sapunov, Vladimir Savinov, Natalia Shusharina

School of Life Sciences Immanuel Kant Baltic Federal University, Kaliningrad, Russia.

*Email: vitaliypetrov@mail.ru Keyword: virtual reality, brain-computer interface, convolutional neural net

INTRODUCTION Currently, virtual reality (VR) technologies come into use in many applications such as medicine, education and entertainment. Traditional ways for user to control virtual objects or avatars are based on mechanical tracking (joysticks, keyboard etc.), optical tracking or voice input [1]. In this work, we propose electromyography (EMG) based finger movement recognition for avatar control applications.

MATERIALS AND METHODS First, EMG signals from hand muscles and corresponding finger bending angles were recorded using BALALAIKA device [2] and LeapMotion. Next, collected data was used for convolutional neural network (CNN) training in order to solve bending state classification problem for each finger independently.

RESULTS Developed CNN was trained and tested using Keras framework. Classification accuracy for right hand fingers is shown in table 1. Table 1. Classification accuracy.

Number of classes

Classification accuracy (per-class average)

Index finger Middle finger Pinky finger Ring finger Thumb

3 0.89 0.89 0.89 0.90 0.88

5 0.77 0.78 0.76 0.77 0.84

7 0.70 0.72 0.71 0.69 0.75

9 0.65 0.66 0.66 0.65 0.68 CONCLUSION Results demonstrated applicability of the proposed approach to avatar control in VR. The future work will be focused on both modification of the CNN structure and accumulation of the experimental data for training.

REFERENCES [1] Fuchs P. Virtual reality headsets-a theoretical and pragmatic approach. – CRC Press, 2017 [2] Shusharina N. N. et al. Multifunctional neurodevice for recognition of electrophysiological signals and data transmission in an exoskeleton construction //Biology and Medicine. – 2016. – Т. 8. – №. 6. – С. 1

Software Platform for MEG-Based Neurofeedback Training

Anna Shishkina1*, Nikolay Smetanin1, Alexey Ossadtchi1

NRU Higher School of Economics, Moscow, Russia

*Email: shishki.ann@gmail.com

INTRODUCTION Neurofeedback is a real-time paradigm which train subject to self-regulate brain functions with providing feedback based on target neural activity by visual, auditory or tactile stimuli. Neurofeedback is considered as a therapeutic approach to treat a range of cognitive, psychiatric and neurological conditions [1]. However, the poor spatial resolution of recordings does not allow the unambiguous modulation of well-identified components of neural activity in targeted brain regions. Real-time magnetoencephalography (MEG) source imaging can provide anatomically-specific neurofeedback to modulate predetermined components of ongoing subjects brain activity. Nowadays there is study demonstrated successful MEG-based neurofeedback training within targeted brain regions of interest related to participants' mental imagery neural activity [2]. In another study the utility of an MEG-based neurofeedback is investigated for voluntary amplitude modulation of sensorimotor mu and beta rhythms [3]. Also, it was demonstrated that MEG-based BCI systems can provide realistic, efficient, and focused neurofeedback to individuals with paralysis and induce the expected neuroplasticity [4]. However, real-time MEG experiments suffer from a serious problem related to source localization error due unaccounted movements of participant's head with respect to the sensor array. These movements lead to topographical blurring of the measurements at the sensor level [5] and subsequent loss of spatial specificity.

To date, the problem of head movement in real-time MEG source-analysis has only been partially addressed. Our goal here is to develop a technological foundation for real-time source-space MEG based neurofeedback experiments taking into account continuous variations in head position.

MATERIALS AND METHODS The experimental data was recorded at the Moscow MEG Centre (the Moscow State University of Psychology and Education) using a 306-channel detector array (Vectorview; Neuromag, Helsinki, Finland). One participant was given the instruction to sit in MEG helmet motionlessly and fix the head in the following anchor positions: base, front, back, left, right and then move the head in a precession-like fashion. Data recording consists of six main 2-minutes sessions with 1 minute breaks between the instructed changes the head position. The first five positions were static, and the last one corresponded to slow precession like movements inside the helmet with approximately constant speed. The data included signals from the four head positioning coils and were sampled at 1000 Hz with a bandwidth of 0.10–330 Hz, and recorded to a file for offline analysis using the Python-MNE toolbox [6]. Instead of solving a full-blown inverse problem and localizing magnetic dipoles corresponding to the four head positioning coils we suggest an alternative approach based on linear interpolation. To do so, using singular value decomposition of the data filtered within the frequency band corresponding to each of the four head positioning coils we extract low

dimensional reference signal subspace for each of the five anchor positions. We then show that it is possible to determine the arbitrary head position based on matching the current coil signals against the extracted reference subspaces with a transform that can be trained based on the head positions extracted off-line. Then, the obtained transform can be used to continuously track head position during the real-time experiment and adjust accordingly the inverse solver coefficients. RESULTS We demonstrate that the proposed approach can be used to deliver sufficiently accurate head position without the need for solving the inverse problem with respect to tracking magnetic dipoles corresponding to the head positioning coils.

REFERENCES [1] Papo, D. (2018). Neurofeedback: Principles, appraisal, and outstanding issues. European Journal of Neuroscience. [2] Florin, E., Bock, E., & Baillet, S. (2014). Targeted reinforcement of neural oscillatory activity with real-time neuroimaging feedback. NeuroImage, 88, 54–60. [3] Mellinger, J., Schalk, G., Braun, C., Preissl, H., Rosenstiel, W., Birbaumer, N., & Kübler, A. (2007). An MEG-based brain–computer interface (BCI). Neuroimage, 36(3), 581-593. [4] Foldes, S. T., Weber, D. J., & Collinger, J. L. (2015). MEG-based neurofeedback for hand rehabilitation. Journal of neuroengineering and rehabilitation, 12(1), 85. [5] Stolk, A., Todorovic, A., Schoffelen, J. M., & Oostenveld, R. (2013). Online and offline tools for head movement compensation in MEG. Neuroimage, 68, 39-48. [6] Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., ... & Hämäläinen, M. S. (2014). MNE software for processing MEG and EEG data. Neuroimage, 86, 446-460.

Foot motor imagery triggered locomotion in exoskeleton: first results with paraplegic patients

Nikolai Smetanin*, Aleksandra Kuznetsova, Marina Ivanova, Alexey Ossadtchi

Centre for Bioelectric Interfaces, HSE, Moscow, Russia *E-mail: n.m.smetanin@gmail.com

INTRODUCTION The development of foot\leg imagery based BCI [1] is challenging as the data analysis procedure needs to solve problems of artifacts in the recorded EEG signals induced by the electrics of exoskeleton, intense body movements and tonic muscle activity [2]. In the joint project with ExoAtlet company and using our recently developed NFBLab software [3] we implemented the BCI-exoskeleton contour: each locomotion cycle is started by foot\leg related motor-imagery.

MATERIALS AND METHODS To extract SMR we used the following pipeline. Firstly, from the beta-band band-passed signals we extracted independent component(s) with topographies corresponding to the leg representation. We then used the envelope of the selected component(s) and applied a simple

threshold rule to detect desynchronization and launch the exoskeleton cycle. The described procedure allows us to focus only on the activity of well established cortical sources in the foot\leg representation area. Thus, the exoskeleton is controlled in a physiologically meaningful way by exploiting the activity of neuronal populations accompanying natural movement initiation.

RESULTS We have tested our implementation in 3 paraplegic patients with spinal cord injury performing EEG-based control of the exoskeleton during the series of walking and resting state blocks which allowed to assess the specificity and the sensitivity of the proposed solution. We have found out that the accuracy of the described classificator reaches its maximum peak at the moment when the locomotor cycle just ends and it gradually decreases during the resting state part. CONCLUSION Based on this and several other observations we suggest a possible protocol for lower-limb BCI controlled exoskeleton therapy that maximizes the count of brain activity initiated locomotion cycles.

REFERENCES [1] He Y., Eguren D., Azorín J., Grossman R., Luu T., Contreras-Vidal J., Brain–machine interfaces for controlling lower-limb powered robotic systems. Journal of Neural Engineering 15(2) (2018) doi: 10.1088/1741-2552/aaa8c0 [2] Kline J. E., Huang H. J., Snyder K. L., Ferris D. P., Isolating gait-related movement artifacts in electroencephalography during human walking. Journal of Neural Engineering. 12 , (2015) doi:10.1088/1741-2560/12/4/046022 [3] Smetanin N., Volkova K., Zabodaev S., Lebedev M.A. and Ossadtchi A., NFBLab—A Versatile Software for Neurofeedback and Brain-Computer Interface Research. Front. Neuroinform. 12:100 (2018) doi: 10.3389/fninf.2018.00100

Hardware-Software Complex of the Mobile Brain-Computer Interface for Technical Means of Rehabilitation

Anastasia Sudareva, Yulia Nekrasova

Moscow State Aviation Institute People with movement disorders do not have the ability to control the means of rehabilitation, which include exoskeletons, artificial limbs and wheelchairs, while their brain activity is usually not disturbed. To establish the connection between the patient and the means of rehabilitation, brain-computer interfaces (BCIs) are developed, allowing a person to interact with the outside world by means of electroencephalogram (EEG). The paper discusses the algorithm for setting up the sensorimotor mobile BCI for a specific patient in order to improve the accuracy and the information transfer rate (ITR). The experiment setup suggested that the subject, depending on the position of the mark on the screen, imagined the movement of both arms or both legs, alternating them with relaxation. The received signals were subjected to segmentation and filtering of artifacts by Independent Component Analysis. Feature extraction was carried out by combining the various time segments into one feature vector in order to take into account the stochastic nature of the EEG with the further application of a Common Spatial Pattern. Machine learning was carried out on the basis of a Linear Discriminant Analysis algorithm. After implementing the search algorithms for localizing

sources of neuronal activity based on the model of equivalent current dipoles, a selection of meaningful channels for a particular patient was carried out, which resulted in a mobile BCI with the optimal number and location of channels (4). To check the quality of the BCI, a working device was designed, as well as a device simulating a technical rehabilitation tool equipped with display means (display and LED indicators). The average correctness of the classification algorithm was 95%, while the ITR increased significantly.

Brain-computer Interface and Artificial Intelligence: Path to Convergence

Boris Vladimirskiy1*, Valeriy Kiroy1+ Center for Neurotechnology, Southern Federal University, Rostov-on-Don, Russia *Email: bmv@sfedu.ru +Email: kiroy@sfedu.ru Keywords : BCI, AI, bioelectrical activity, analysis, psychophysiology, cognitive processes

The next few years will see a convergence of two technologies, BCI (brain-computer interface) and AI (artificial intelligence), which will lead to the emergence of new ideas, research results, and novel applications, many yet to be conceived. This stems from the current paradigm challenges being faced by these technologies and is based on the latest advances in neurotechnology, algorithms, and machine learning software, as well as the successful modelling of a number of neurophysiological processes. An important achievement in psychophysiology of the last few decades is the experimental and model-based verification of the brain’s ability, as it were, to turn software into hardware and vice versa, i.e., wiring commands into the short-term memory and then extracting them as "programs" specifying perceptual processes and behavioural control. The information can be supplied by information signals per se or, e.g., by snippets of instructions to perform motor activity. All of the above forms the theoretical basis for brain-computer interfaces. In most existing stimulus-independent BCIs commands corresponding to the simplest motor acts only (including ideomotor ones) are identified out of the brain activity. The number of such commands is relatively low and progress in this area has stagnated recently. Thus, further analysis and adcancement of approaches to mental command representation training is necessary. This is in order to develop algorithms and software for real-life use which could reliably classify a larger number of classes of mental commands corresponding to more complex cognitive processes. That falls into the realm of AI. What we mean by the convergence of the BCI and AI is not only – and not so much – the mutual influence, but also the cross-fertilization and confluence of these technologies, which should lead to the emergence of a new field called PASAlogy (Procuratio actionis cerebro analysim – analysis and control of brain activity). Discussing the major goals and challenges of PASAlogy will be the focus of the present communication.

EEG-based classification of the intentional and spontaneous selection of moving objects with gaze

Darisy Zhao*, Anatoly Vasilyev, Bogdan Kozyrsky, Eugeny Melnichuk, Sergei Shishkin

NRC “Kurchatov Institute”, Moscow, Russia *E-mail: chzhao@phystech.edu

Keywords: eye-brain-computer interface, EBCI, gaze interaction, gaze-based input, selection, smooth pursuit eye movements, moving objects

INTRODUCTION Gaze-based input to computers is associated with frequent false triggering due to similarity of intentional and spontaneous gaze behavior. A special passive BCI can be developed to differentiate them based on the accompanying EEG [1, 2]. Recently, we applied this approach to the selection of moving objects with smooth pursuit eye movements [3]. However, the classification of intentional and spontaneous conditions was inaccurate, possibly because of “contamination” of the spontaneous condition with intention.

METHODS In the current study, we added a condition where static objects should be selected, to compare the EEG under this condition and the moving object conditions, and run a longer experiment series (14 participants). The “selection” tasks were to select moving (6.8 ̊/s) or static numbered balls in ascending/descending order. A ball was “selected” (highlighted) based on the median of distances between its center and gaze position in a moving window. To provoke spontaneous pursuit, we used tasks without ball highlighting: (1) find a slightly faster ball; (2) find 5 balls with a certain number of dots and summate their numbers.

RESULTS The EEG was strikingly similar between the intentional fixation-based and pursuit-based selections. LDA with shrinkage regularization classified the intentional vs. spontaneous smooth pursuits with ROC AUC 0.63±0.07 (M±SD), comparable to typically obtained for static selection with linear classifiers.

CONCLUSION The EEG accompanying moving object selection with gaze can be used to classify the intentional selections against spontaneous pursuits.

ACKNOWLEDGEMENT This work was partly supported by the Russian Science Foundation, grant 18-19-00593 (design of the feature sets and study of the classifier performance). REFERENCES [1] J. Protzak, K. Ihme, T.O. Zander. A passive brain-computer interface for supporting gaze-based human-machine interaction. UAHCI, 2013, pp. 662–671. [2] S.L. Shishkin, Y.O. Nuzhdin, E.P. Svirin, A.G. Trofimov, A.A. Fedorova, B.L. Kozyrskiy, B.M. Velichkovsky. EEG negativity in fixations used for gaze-based control: Toward

converting intentions into actions with an eye-brain-computer interface. Front. Neurosci. 2016. 10:528. [3] D.G. Zhao, A.V. Isachenko, E.V. Melnichuk, S.L. Shishkin. Eye-brain-computer interfacing with smooth pursuit eye movements. The 4th Int. Conf. BCI: Science and Practice, 2018, p. 21.

Symposium VR technologies in medical and social rehabilitation

(in Russian)

Виртуальные технологии в реабилитации больных рассеянным склерозом и роль пациентского сообщества в развитии системы здравоохранения

Ян Власов*, Лина Жаворонкова

1Самарский государственный медицинский университет, Самара. Россия;

*E-mail: sams99@inbox.ru

ВВЕДЕНИЕ Одним из приоритетных направлений деятельности системы здравоохранения и социального развития Российской Федерации является создание и внедрение в практику новейших технологий реабилитации лиц с нарушением двигательных и когнитивных функций. МАТЕРИАЛЫ И МЕТОДЫ При поддержке фонда президентских грантов на базе Самарской региональной общественной организации инвалидов больных рассеянным склерозом реализуется программа кардинально иного (инновационного) подхода к процессу реабилитации лиц с ограниченными возможностями путем внедрения в процесс реабилитации VR- технологий, что позволяет повысить качество жизни данной категории пациентов, позволит улучшить их социальную адаптацию, даст социально-экономический эффект [1]. РЕЗУЛЬТАТЫ Технологии виртуальной реальности активно внедряются в деятельность реабилитационных центров Самарской области. Ведется масштабная информационная поддержка проекта через региональные и федеральные СМИ. Налаживаются контакты для тиражирования проекта. Вырабатываются новые методы реабилитации лиц с ограниченными возможностями на территории области. Социальный эффект выражается в ускоренном процессе восстановления пациентов, в частности, перенесших инсульты с нарушением двигательных и/или когнитивных функций, т.е. возвращение в социальную структуру общества полноценных граждан после перенесенного заболевания [2]. ЗАКЛЮЧЕНИЕ. В ходе реализации проекта разработана и внедрена VR-технология в медико-социальную реабилитацию лиц с ограниченными возможностями. Решен комплекс взаимосвязанных задач: расширение и персонификация сценариев виртуальной реальности и тактики ведения пациентов путем разработки виртуальных сред – аналогов социальной среды, организация взаимодействия врач-пациент путем обучения пациента использованию виртуальной реальности на дому и дистанционного контроля врачом динамики реабилитации.

СПИСОК ЛИТЕРАТУРЫ: 1 Heller A, Wade DT, Wood VA, et al. Arm function after stroke: measurement and recovery over the first three months. J Neurol Neurosurg Psychiatry. 1987;50(6):714-9. 2 Nakayama H, Jorgensen HS, Raaschou HO, et al. Recovery of upper extremity function in stroke patients: the Copenhagen Stroke Study. Arch Phys Med Rehabil. 1994;75(4):394-8.

Опыт применения VR в реабилитации пациентов, страдающих рассеянным склерозом

Александр Воронин

Самарский государственный медицинский университет, Самара. Россия; E-mail: alek.voronin86@yandex.ru ВВЕДЕНИЕ Виртуальные технологии являются новым вариантом лечения, который может обеспечить высокие дозы регулярных специальных упражнений, подобранных для конкретного пациента [1], виртуальная реальность определяется как " использование интерактивных симуляций, созданных с помощью компьютерного оборудования и программного обеспечения, чтобы предоставить пользователям возможность участвовать в средах, которые имитируют реальные объекты и события”[2]. ЦЕЛЬ И ЗАДАЧИ Изучить и проанализировать применение виртуальных технологий, как альтернативу существующим методикам реабилитации. МАТЕРИАЛЫ И МЕТОДЫ Самарская региональная общественная организация инвалидов-больных рассеянным склерозом совместно с Самарским государственным медицинским университетом проводили школы пациентов.На занятиях неврологи и реабилитологи рассказывали о базовых принципах работы с виртуальной реальностью, обучали использованию технологичного оборудования для реабилитации. Работа школ была организована в рамках проекта «Безграничная реальность: технологии виртуальной реальности для медико-социальной реабилитации», ставшего победителем второго конкурса Президентских грантов 2018 года. Врамка встреч с ведущими реабилитологами области, пациенты с рассеянным склерозом смогли больше узнать о своем заболевании, современных методах реабилитации и задать специалистам вопросы, на которые не остается времени во время плановых приемов в поликлиниках. РЕЗУЛЬТАТЫ За время проекта было проведено более 700 курсов реабилитации. Проведено более 30 просветительских школ. Положительный результат от курсов реабилитации ощутили более 94% пациентов. ЗАКЛЮЧЕНИЕ Люди, страдающие рассеянным склерозом, постоянно нуждаются в посторонней помощи. Они не в состоянии самостоятельно приготовить еду, переодеться, сделать уборку или сходить в магазин за покупками. Виртуальная реальность с этими привычными сценариями должна помочь им восстановить утерянные навыки, что, в конечном счете, должно значительно повысить качество жизни самих пациентов и их родственников, способствовать успешной социализации больных. СПИСОК ЛИТЕРАТУРЫ 1. Demain S, Burridge J, Ellis-Hill C, et al. Assistive technologies after stroke: selfmanagement or fending for yourself? A focus group study. BMC Health Serv Res. 2013;13:334. 2. Weiss P, Kizony R, Feintuch U, et al. Virtual reality in neurorehabilitation. In: Selzer M CL,

Gage F, Clarke S, Duncan P, editor. Cambridge: Cambridge University Press; 2006.

Соматосенсорная активация как способ увеличения мощности сенсомоторных ритмов для использования в интерфейсе мозг-компьютер

Татьяна Веселова Самарский государственный медицинский университет, Самара, Россия E-mail: samaranev@mail.ru ВВЕДЕНИЕ Воображение движения является ключевой способностью человека, необходимой для управления сигналами ЭЭГ в интерфейсе мозг- компьютер (ИМК), и развития, таким образом, взаимодействия с окружающей средой, особенно при патологии нервно-мышечной системы. Становление навыка воображения движения и инициации при этом доминантных паттернов ЭЭГ, поддающихся классификации, остается актуальной проблемой. Основной задачей является изучение возможности дифференцировки движений отдельных частей конечности и повышения надежности ИМК. [1,2] ЦЕЛЬ ИССЛЕДОВАНИЯ Изучение способов ускорения обучения воображению движения и эффективно включать сигналы ЭЭГ в управление системой «двигательное воображение- интерфейс мозг-компьютер» (ДВ-ИМК). Материалы и методы: В исследовании приняло участие 11 здоровых людей в возрасте 18–20 лет. ЭЭГ регистрировалась монополярно с помощью 128- канальной системы записи ЭЭГ (BP-010302 BrainАmp Standart 128) в состоянии спокойного бодрствования и во время воображения двухвекторных движений в доминантной руке при следующих условиях: без предварительной соматосенсорной активации, сразу после 30-сек стереотипной проприоцептивной стимуляции сокращения мышц руки в режимах низкоинтенсивной и высокоинтенсивной стимуляции, а также через 15 мин после рекрутировки двигательного паттерна. РЕЗУЛЬТАТЫ

После проприоцептивной стимуляции мышц предплечья воображение сгибания пальцев руки сопровождалось изменениями в альфа2- диапазоне ЭЭГ: после низкоинтенсивной соматосенсорной активации фиксировались синхронизация и десинхронизация ритмов альфа и бетта диапазонов; максимальное количество достоверных ЭЭГ-отведений отмечалось у 40% испытуемых. После высокоинтенсивного режима превалировала реакция десинхронизации сенсомоторных ритмов; уменьшалось количество достоверных отведений (р≤0,001) (по сравнению с низкоинтенсивным режимом – у 72,7% испытуемых, по сравнению с воображением без активации – у 54,5%). Через 15 мин после проприоцептивной активации воображение сгибания в локтевом суставе вызывало реакцию десинхронизации в альфа2-дипазоне у 66,7% испытуемых, у 22,2% - в равной степен были обнаружены синхронизация и десинхронизация в данном частотном диапазоне. Количество достоверных ЭЭГ-отведений уменьшалось у 55,6% испытуемых, у 22,2% испытуемых – увеличивалось. Что можно характеризовать как увеличение локальности происходящих изменений.

ЗАКЛЮЧЕНИЕ Показано, что соматосенсорная активация мышц руки увеличивает реакции синхронизации и десинхронизации в альфа-2 и бета-2 ритмах ЭЭГ при воображении движения. После сенсомоторной активации высокой интенсивности при воображении движения превалирует десинхронизация в альфа2-диапазоне ЭЭГ с уменьшением числа достоверных отведений по сравнению с низкоинтенсивным режимом сенсомоторной активации. СПИСОК ЛИТЕРАТУРЫ 1. Blankertz B., Lemm S., Treder M., Haufe S., Müller K.R. Single-trial analysis and classification of ERP components– a tutorial. Neuroimage. 2011. 56(2): 814–825. 2. Cecotti H., Ries A.J. Best practice for single-trial detection of event-related potentials: Application to brain-computer interfaces. International Journal of Psychophysiology. 2017. 111: 156–169.

Мультимодальные вызванные потенциалы в диагностике активности течения рецидивирующе-ремиттирующего рассеянного склероза

Алексей Нилов Самарская областная клиническая больница, Самара, Россия Email: samaranevr@mail.ru ВВЕДЕНИЕ Мультимодальные вызванные потенциалы (МВП), по данным некоторых исследований, касающихся пациентов с рассеянным склерозом (РС), позволяют осуществлять контроль заболевания и прогнозировать прогрессирование инвалидизации [1]. Эти исследования показывают перспективность использования методики мультимодальной регистрации вызванных потенциалов (ВП) при оценке прогноза заболевания и эффективности его лечения тем или иным препаратом, изменяющим течение заболевания [2]. Недостатком проведенных исследований является малочисленность групп испытуемых, а также отсутствие линейных зависимостей показателей с активностью заболевания. Поиск прогностических маркеров позволит сформировать представление об активности течения заболевания в краткосрочной перспективе наблюдения [3]. ЦЕЛЬ Изучение мультимодальных вызванных потенциалов (МВП) в группе с активным течением рассеянного склероза (РС) и в группе пациентов с РС без обострений в течение 12 месяцев наблюдения. Материалы и методы. В исследование включены 32 пациента с установленным диагнозом: «клинически достоверный рассеянный склероз, рецидивирующе- ремитирующее течение». Проводилась оценка статуса по шкале EDSS. Всем больным проведено исследование МВП. Длительность наблюдения за пациентами составила 12 месяцев. Все обследованные пациенты разделены на две группы в зависимости от наличия обострений заболевания к концу периода наблюдения.

РЕЗУЛЬТАТЫ Доступность клинического использования МВП в последние годы повлекла за собой появление публикаций по использованию данной методики в прогнозе заболеваний, сопровождающихся демиелинизацией или аксональным повреждением центральной нервной системы (ЦНС). Кроме того, данная методика оценивает функциональные изменения, происходящие в ЦНС, до появления структурных дефектов, визуализируемых с помощью, например, МРТ. Именно поэтому МВП, возможно, имеют большой потенциал именно для краткосрочной оценки течения РС, а также для прогнозирования трансформации ранних форм заболевания (КИС и РИС) в клинически достоверный рассеянный склероз или в переходе РРРС в ВПРС. СПИСОК ЛИТЕРАТУРЫ 1. Kallmann BA, Fackelmann S, Toyka KV, Rieckmann P, Reiners K. Early abnormalities of evoked potentials and future disability in patients with multiple sclerosis. Mult Scler 2006; 12: 58–65. 2. Invernizzi P, Bertolasi L, Bianchi MR, Turatti M, Gajofatto A, Benedetti MD. Prognostic value of multimodal evoked potentials in multiple sclerosis: the EP score. J Neurol 2011; 258: 1933–9. 3. Schlaeger R, D’Souza M, Schindler C, Grize L, Kappos L, Fuhr P. Electrophysiological markers and predictors of the disease course in primary progressive multiple sclerosis. Mult Scler 2014; 20: 51–6.

Использование методики виртуальной реальности у пациентов с острым нарушением мозгового кровообращения

Ксения Моисеева

Самарский государственный медицинский университет, Самара, Россия. E-mail: xenia.moi5eewa@yandex.ru

ВВЕДЕНИЕ Острое нарушение мозгового кровообращения является основной причиной инвалидизации [1]. Нарушение двигательных функций приводит к ограничению мобильности пациента. Применение методики на основе виртуальной реальности способствует улучшению функции как верхних, так и нижних конечностей у пациентов после перенесенного инсульта, так как способствуют интеграции пользователей в среду [2]. ЦЕЛЬ И ЗАДАЧИ Изучение эффективности использования иммерсивной виртуальной реальности с проприоцептивным сенсорным воздействием на восстановление статолокомоторной функции у пациентов в остром периоде ишемического инсульта МАТЕРИАЛ И МЕТОДЫ В исследование включено 33 пациента в остром периоде ишемического инсульта в каротидном бассейне. Пациенты рандомизированы в две группы: основная группа дополнительно получала занятия в условиях иммерсивной виртуальной реальности с сенсорным воздействиемпродолжительностью 10 занятий по 15 минут, группа сравнения получала стандартный объем реабилитационной помощи.

РЕЗУЛЬТАТЫ У пациентов основной группы отмечалось улучшение статолокомоторных функций по данным шкалы баланса Берга уже на шестой день занятий (р=0,03). Различия между сравниваемыми группами в последний день реабилитации демонстрировал улучшение статолокомоторной функции на 23 балла (95 % ДИ 13–27 баллов) в исследуемой группе и на 7 баллов (95 % ДИ 2–13 баллов) по шкале баланса Берга. ЗАКЛЮЧЕНИЕ Проведенное исследование показало эффективность использования иммерсивной виртуальной реальности с проприоцептивным сенсорным воздействием при восстановлении статолокомоторной функции у пациентов в остром периоде ишемического инсульта. Использование данного метода двигательной реабилитации демонстрирует безопасность его использования у пациентов в остром периоде ишемического инсульта [3]. Возможно увеличение эффективности использования иммерсивной виртуальной реальности за счет мультисенсорной обратной связи или технологии нейрокомпьютерного интерфейса. СПИСОК ЛИТЕРАТУРЫ: 1. Захаров А.В., Пятин В.Ф., Колсанов А.В., Повереннова И.Е., Сергеева М.С., Хивинцева Е.В., Коровина Е.С., Куцепалова Г.Ю. Использование виртуальной реальности в качестве средства ускорения двигательной реабилитации пациентов после перенесенного острого нарушения мозгового кровообращения//Наука и инновации в медицине, № 3 (3), 2016 - с. 62-66. 2. Хивинцева Е.В., Сергеева М.С., Пятин В.Ф., Колсанов А.В., Захаров А.В., Антипов О.И., Коровина Е.С. Динамика сенсомоторной активности коры головного мозга при интенции движения //Нейрокомпьютеры: разработка, применение, № 6, 2016 - с. 40-43. 3. Пятин В.Ф., Колсанов А.В, Захаров А.В., Сергеева М.С. Восстановление двигательной активности нижних конечностей у пациентов в остром периоде острого нарушения мозгового кровообращения за счет виртуальной вертикализации // Избранные вопросы нейрореабилитации. Материалы IX международного конгресса "Нейрореабилитация - 2017", с. 169-170.

Опыт реабилитации в условия виртуальной реальности пациентов с болезнью Паркинсона

Д.К.Шелудякова, А.А.Кузнецов Самарский государственный медицинский университет, Самара, Россия E-mail: herecouldbeyourad@yandex.ru ВВЕДЕНИЕ Болезнь Паркинсона (БП) является вторым по частоте (после болезни Альцгеймера) нейродегенеративным заболеванием. По данным разных исследований, распространенность БП варьирует от 60 до 160 на 100 тыс. населения. Заболеваемость БП составляет от 12 до 20 на 100 тыс. в год. Через 10–20 лет около 50% выживших требуют постоянного постороннего ухода. Цель исследования заключалась в изучении влияния реабилитации в виртуальной реальности (ВР) на моторные функции пациентов с БП. Был проведен анализ 15 пациентов с БП в возрасте 64 [54; 69] лет. Пациенты получали терапию леводопой , в качестве методов реабилитации с пациентам проводились занятия в виртуальной реальности с имплицитной демонстрацией движения по горизонтальной поверхности и

сенсорным подтверждением успешности данного действия за счет стимуляции проприоцепторов стопы. Оценка двигательной функции проводилась по унифицированной шкале оценки БП международного общества расстройств движений (MDS UPDRS). В качестве статистического анализа использовался метод оценки зависимых групп сравнения не имеющих нормального распределения. На момент включения в исследование пациенты характеризовались следующими показателями по анализируемым разделам шкалы UPDRS. Балл оценки моторных аспектов повседневной жизни составил 7 [4;13], по разделу оценки двигательной функции 29 [23;49] баллов. По окончании реабилитационных занятий балл по шкале оценки моторных аспектов повседневной жизни составил 3 [3;18], а по разделу оценки двигательной функции 20 [13;42] баллов. Отмечается статистически достоверное улучшение моторных функций у пациентов после проводимой реабилитации в ВР. Полученные результаты позволяют предположить, что для пациентов с экстрапирамидной патологией занятия ВР могут оказывать положительное воздействие на моторные проявления заболевания. СПИСОК ЛИТЕРАТУРЫ 1. Захаров А.В., Кузнецова Н.И., Хивинцева Е.В., Власов Я.В. Особенности реабилитации при рассеянном склерозе // Неврологический вестник - 2010 - No 13(1). - С. 110-114. 2. Котов С.В., Турбина Л.Г., Бобров П.Д., Фролов А.А., Павлова О.Г., Курганская М.Е., Бирюкова Е.В. Реабилитация больных, перенесших инсульт, с помощью биоинженерного комплекса «интерфейс мозг – компьютер + экзоскелет» // Журнал неврологии и психиатрии им. С.С. Корсакова. – 2014 – Т.114, No12. – С. 66-72. 3. Сергеева М.С., Пятин В.Ф., Колсанов А.В., Захаров А.В., Антипов О.И.,Коровина Е.С. Модуляция сенсомоторных ритмов электроэнцефалограммы // Биомедицинская радиоэлектроника. ‒ 2016. - No 5. 2016 - No 5‒ 2016. - No 5. C. 28-30.

Сравнение классификаторов в задаче классификации когнитивных вызванных потенциалов

Михаил Курапов

Самарская областная клиническая больница, Самара, Россия E-mail: samaranev@mail.ru ВВЕДЕНИЕ Одно из центральных направлений в области нейротехнологий связано с созданием нейрокомпьютерных интерфейсов (НКИ). В ряде случаев в таких интерфейсах управляющие сигналы (команды) формируются на базе характеристик зрительных вызванных потенциалов (ЗВП), возникающих как реакция головного мозга человека на предъявление визуальных стимулов [1]. Активно проводятся исследования, направленные на улучшение алгоритмов классификации ЗВП [2]. ЦЕЛЬ ИССЛЕДОВАНИЯ – сравнение работы классификаторов, достаточно часто используемых в задаче распознавания целевых и нецелевых единичных зрительных вызванных потенциалов коры головного мозга человека в системаx НКИ.

МАТЕРИАЛЫ И МЕТОДЫ В работе были использованы данные, предоставленные в открытый доступ группой исследователей и размещённые по ссылке: ftp://climb.genomics.cn/pub/10.5524/100001_101000/100111/ Указанная база данных содержит электроэнцефалограммы, зарегистрированные у 19 испытуемых при предъявлении зрительных стимулов в рамках трёхстимульной парадигмы – разновидности эксперимента odd-ball. ЭЭГ регистрировалась 19-канальным электроэнцефалографом и представлена в формате BrainVision. РЕЗУЛЬТАТЫ Проведенное исследование точности классификаторов, с одной стороны, дополняет и расширяет хорошо известные работы. Проведено сравнение точности классификаторов Fisher LDA, swLDA, PCM, SVM linear, SVM gaussian. С другой стороны, в нашей работе оценивается точность классификации новых алгоритмов, которые ранее не сравнивались между собой. ЗАКЛЮЧЕНИЕ В работе не ставилась задача получения высокой точности классификации. Проводилось сравнение точности классификации нескольких классификаторов на одинаковых входных данных и в одних и тех же условиях. Наряду с линейными классификаторами семейства LDA, представленными в работах, в НКИ не менее эффективно может использоваться классификатор averaged NNet на базе простой нейронной сети, который хорошо себя показал при решении задачи двоичной классификации целевых и не целевых единичных ЗВП. Повышение точности классификатора averaged NNet возможно при применении предварительной обработки ЭЭГ-сигнала, использовании пространственной фильтрации и различных методов снижения размерности классифицирующих признаков, а также настройки параметров самого классификатора. СПИСОК ЛИТЕРАТУРЫ 1. Guger C., Krausz G., Allison B.Z., Edlinger G. Comparison of dry and gel based electrodes for P300 brain-computer interfaces. Front. Neurosci. 2012. 6: 60. 2. Cecotti H., Ries A.J. Best practice for single-trial detection of event-related potentials: Application to brain-computer interfaces. International Journal of Psychophysiology. 2017. 111: 156–169.

Изменения мультимодальных вызванных потенциалов при клинически изолированном синдроме

Долгих Татьяна Самарская областная клиническая больница, Самара, Россия E-mail: samaranevr@mail.ru ВВЕДЕНИЕ Мультимодальные вызванные потенциалы (МВП), по данным некоторых исследований, касающихся пациентов с рассеянным склерозом (РС), позволяют осуществлять

контроль заболевания и прогнозировать прогрессирование инвалидизации [1]. Эти исследования показывают перспективность использования методики мультимодальной регистрации вызванных потенциалов (ВП) при оценке прогноза заболевания и эффективности его лечения тем или иным препаратом, изменяющим течение заболевания [2]. Первая атака демиелинизирующего процесса - изолированный синдром (КИС) определяется как отдельный клинический эпизод, вызванный повреждением одного или нескольких отделов центральной нервной системы, в основе которого лежит, предположительно, воспалительно-демиелинизирующий процесс. Поиск прогностических маркеров позволит сформировать представление об активности лечения заболевания в краткосрочной перспективе наблюдения и что самое главное позволит прогнозировать динамику клинических форм заболевания [3]. Цель работы ― оценить изменения мультимодальных вызванных потенциалов у пациентов с клинически изолированным синдромом (КИС). Оценить их вклад в расширение формулировки «диссеминации в пространстве», необходимого для постановки клинически достоверного рассеянного склероза. МАТЕРИАЛЫ И МЕТОДЫ Проведено обследование по акустических стволовых, зрительных и соматосенсорных вызванных потенциалов 30 пациентов с монофокальным КИС и 13 пациентов с мультифокальным КИС. Исследуемые группы были сопоставимы по выраженности неврологического дефицита и возрасту. РЕЗУЛЬТАТЫ По данным зрительных вызванных потенциалов различий между исследуемым группами получено не было. Отмечается увеличение латентности акустического межпикового интервала I-V до 3,8 мс. (р=0,03), а также латентности пика N11 соматосенсорного вызванного потенциала до 10,3 мс. (р=0,04) у пациентов с мультифокальным КИС. Определение изменений соматосенсорных вызванных потенциалов отражает субклиническое поражение шейного отдела спинного мозга. ЗАКЛЮЧЕНИЕ Применение мультимодальных вызванных потенциалов позволяет выявлять субклинические очаги демиелинизации, что может, служить подтверждением положения «диссеминации в пространстве» при определении достоверного рассеянного склероза СПИСОК ЛИТЕРАТУРЫ 1. Повереннова И.Е., Романова Т.В., Захаров А.В., Хивинцева Е.В. Раннее выявление когнитивных нарушений у пациентов с рассеянным склерозом // Саратовский научно-медицинский журнал. ― 2017. ― Т. 13, No1. ― С. 164-168. 2. Захаров А.В., Власов Я.В., Повереннова И.Е., и др. Oсобенности постуральных нарушений у больных рассеянным склерозом // Журнал неврологии и психиатрии им. C.C. Корсакова. ― 2014. ― Т. 114, No2-2. ― С. 55-58 3. Захаров А.В., Повереннова И.Е., Хивинцева Е.В., и др. Анализ вероятности перехода монофокального клинически изолированного синдрома в клинически достоверный рассеянный склероз // Саратовский научно-медицинский журнал. ― 2012. ― Т. 8б, No2. ― С. 432-435.

Использование комитета нейронных сетей при классификации идеомоторного движения

Максим Елизаров Самарский государственный медицинский университет, Самара, Россия E-mail: maksim.elizarov@mail.ru ВВЕДЕНИЕ

При анализе электроэнцефалограммы (ЭЭГ), решения обратных задач ЭЭГ и построения классификаторов для интерпретации ЭЭГ активности используются различные нейросетевые методы. [1] Анализ пространственно – временных паттернов с помощью предлагаемых классификаторов позволяет индивидуализировать изменения в ЭЭГ и использовать их в контексте «интерфейс мозг-компьютер» (МКИ). [2] Целью работы являлось изучение возможности применения комитета различных классификаторов в улучшении работы МКИ. МАТЕРИАЛЫ И МЕТОДЫ Известно, что человек способен произвольно изменять электрическую активность своего мозга с помощью мысленно выполняемых движений, генерируя паттерны, которыми можно управлять внешними устройствами. Обнаружение соответствующих паттернов в электроэнцефалограмме позволяет классифицировать намерения пользователя с тем, чтобы реализовать последующую трансляцию регистрируемой мозговой активности в сообщения или команды для внешнего устройства В задачи исследования входила разработка методики и средств классификации ЭЭГ-паттернов воображаемых движений и сравнительной оценке их эффективности. Классификация ЭЭГ-паттернов воображаемых движений будут проводится с использованием комитета искусственных нейронных сетей, обученных методом: обратного распространения ошибки и нейронной сети Кохонена. РЕЗУЛЬТАТЫ Классификатор паттернов электроэнцефалограммы мысленных движений состоит из двух нейронных сетей, применяемых для классификации каждого из типов мысленных движений – движений левой и правой рукой, и одной нейронной сети для разделения электроэнцефалограммы движений и фоновой электроэнцефалограммы, записанной в отсутствии каких либо движений. Для учета временной структуры паттернов электроэнцефалограммы использован интерпретатор последовательности ответов нейронных сетей. В работе предложен вариант нейросетевого подхода для классификации пространственно временных паттернов электроэнцефалограммы мозговой активности асинхронно выполняемых мысленных движений. В основе классификатора лежит комитет нейросетей радиально-базисных функций и интерпретатор их ответов. Особенностью нейросетей явилась реализация ими положительных ответов о принадлежности только к одному классу, тогда как для большого многообразия примеров других классов нейронная сеть не дает никакого ответа. Другой особенностью модели классификатора явилось наличие интерпретатора последовательности ответов нейросетей для учета временной структуры распознаваемых паттернов, тогда как традиционное решение основано на

расширении количества входов нейросети для учета предыдущих значений временного ряда. ЗАКЛЮЧЕНИЕ Классификатор продемонстрировал убедительную точность распознавания паттернов мысленных движений и отсутствие ложных паттернов движений для состояния покоя. СПИСОК ЛИТЕРАТУРЫ 1. Шепелев И.Е., Владимирский Б.М. «Построение нейросетевого классификатора для интерфейса «мозг-компьютер»». Нейрокомпьютеры: разработка, применение, 2010, No2. с. 4-10. 2. Антипов О.И., Захаров А.В., Неганов В.А. Сравнение скорости и точности фрактальных методов детерминированного хаоса применительно к распознанию стадий сна Бюллетень Восточно-Сибирского научного центра Сибирского отделения Российской академии медицинских наук. 2013. No 2-1 (90). С. 9-14.

«VR-движение»: программа VR-тренировок для дополнения стандартных методов реабилитации пациентов с моторными нарушениями

Муравьев Н.В. 1 , Жданов М.А. 2 , Шнайдер Г.В. 3 , Апарина К.В. 4 , Каменских Е.М. 5 1ООО «Движение», Томск; 2ООО «Геос», Томск; 3ФГБОУ ВО «Сибирский государственный медицинский университет» Минздрава России, Томск; 4НИ ТГУ, Томск; E-mail: gershnayder@gmail.com Ключевые слова: персонализированная реабилитация, виртуальная реальность, eye-tracking, моторные нарушения, детский церебральный паралич, острое нарушение мозгового кровообращения, черепно-мозговая травма. ВВЕДЕНИЕ Ускоренное технологическое развитие таких перспективных направлений, как виртуальная и дополненная реальности, нейроинтерфейсы и полимодальная стимуляция открывают новые возможности в лечебно- диагностическом процессе [1]. Отчасти, это происходит за счет повышения доступности и универсальности удаленного применения данных методик. В настоящий момент имеются подтвержденные данные о повышении эффективности восстановления сенсорно-моторных функций посредством virtual reality (VR)-тренировок [2,3]. Реабилитационная программа упражнений в виртуальном окружении способствует стимуляции процессов синаптогенеза и формированию новых сенсомоторных стереотипов [4]. Цель и задачи: Разработка программы VR-тренировок для дополнения стандартных методов реабилитации пациентов с моторными нарушениями. МАТЕРИАЛЫ И МЕТОДЫ Командой была разработана технология «VR-движение», которая реализовывалась на платформе VR-очков: пациент за счет технологии eye-tracking мог совершать движения

верхними и нижними конечностями и выполнять наборы специализированных упражнений. Для начальной апробации программы «VR-движение» были отобраны участники с детским церебральным параличом (ДЦП), с состояниями после нарушений мозгового кровообращения, черепно-мозговой или спинной травмы. РЕЗУЛЬТАТЫ В процессе проведения тренировок с 10 добровольцами было выявлено, что разработанная технология не вызывает побочных явлений в виде головокружения, падений, тошноты и рвоты. Все пациенты выразили заинтересованность в проводимой методике, также 90% отмечали парестезии, локальное повышение температуры в парализованных конечностях и субъективное повышение тонуса и силы в мышцах, на которые были направлены упражнения. ЗАКЛЮЧЕНИЕ Применение разработанной технологии «VR-движение» способствует положительным изменениям на психоэмоциональном фоне, что выражается за счет субъективных характеристик патологического состояния. Однако для более детального изучения методики необходимо расширение объема выборки участников, применение валидизированных клинических методов оценки состояния и нейрофизиологических методов (электронейромиография). Более того, рассматриваются способы интеграции технологии с биологической обратной связью (БОС). СПИСОК ЛИТЕРАТУРЫ 1. Wilson, B.A. (2008). Neuropsychological Rehabilitation. Annual Review of Clinical Psychology, 4:1, 141- 162 2. Dell, M.W., Lin C.-C. D., Harrison, V. (2009). Stroke Rehabilitation: Strategies to Enhance Motor Recovery. Annual Review of Medicine, 60:1, 55-68 3. Edgerton, V.R., Tillakaratne, N.J.K., Bigbee, A.J., D. de Leon, R., Roy, R.R. (2004). Plasticity of the spinal circuitry after injury. Annual Review of Neuroscience, 27:1, 145-167 4. Хижникова А.Е., Клочков А.С., Котовсмоленский А.М., Супонева Н.А., Черникова Л.А. (2016). Виртуальная реальность как метод восстановления двигательной функции руки. Анналы клинической и экспериментальной неврологии, №3.