Transcript of BMI Principles Jose C. Principe University of Florida Adapted from Hayrettin Gürkök, U. of Twente,...
Slide 1
BMI Principles Jose C. Principe University of Florida Adapted
from Hayrettin Grkk, U. of Twente, NL
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Literature
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Difficulties in Invasive BMIs BCIs offer an easy entry to
research Non invasiveness straight forward data collection Closer
to cognition Conventional signal processing BMIs research
infrastructure is much harder Work with animals (ethics) Difficult
instrumentation Unclear signal processing
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Choice of Scale for Neuroprosthetics Bandwidth (approximate)
Localization Scalp Electrodes 0 ~ 80 HzCortical Surface Volume
Conduction 3-5 cm Electro- corticogram (ECoG) 0 ~ 500HzCortical
Surface 0.5-1 cm Micro Electrodes 0 ~ 500Hz 500 ~ 7kHz Local Fields
1mm Single Neuron 200 m
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Electrode Arrays J. C. Sanchez, N. Alba, T. Nishida, C. Batich,
and P. R. Carney, "Structural modifications in chronic microwire
electrodes for cortical neuroprosthetics: a case study," IEEE
Transactions on Neural Systems and Rehabilitation Engineering, 2006
Utah array Brain Gate Michigan probes
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Technical Issues with BMIs An implantable BMI requires beyond
of state of the art technology: Ultra low power Ultra miniaturized
Huge data bandwidth/power form factor Packaging
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28mm 15mm 12mm Thru vias to RX/Power Coil + 12.5 mm Coil
winding 3.5 mm 50m pitch Electrodes Coin Battery (10 x 2.5 mm) Thru
vias to Battery Supporting screws Flexible substrate TX antenna
Modular Electrodes Electrode attachment sites IF-IC RFIC 18 mm Coil
Battery Patterned Substrate Supporting Substrate Electrode Array IC
Flip-chip connection Specifications: 16 flexible microelectrodes
(40 dB, 20 KHz) Wireless (500 Kpulse/sec) 2mW of power (72-96 hours
between charges) FWIRE: Florida Wireless Implantable Recording
Electrodes
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RatPack Low-Power, Wireless, Portable BMIs Requirements Total
Weight: < 100g Battery Powered: Run for 4 hours Implantable
Biocompatible Heat flux: < 50 mW/cm 2 Power dissipation should
not exceed a few hundred milliwatts Backpack Small form factor
Speed vs. Low Power
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UF PICO System PICO system = DSP + Wireless Generation 3
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J.R. Wolpaw et al. 2002 BCI (BMI) bypasses the brains normal
pathways of peripheral nerves (and muscles) General
Architecture
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BMIs: How to put it together? NeoCortical Brain Areas Related
to Movement Posterior Parietal (PP) Visual to motor transformation
Premotor (PM) and Dorsal Premotor (PMD) - Planning and guidance
(visual inputs) Primary Motor (M1) Initiates muscle
contraction
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Motor Tasks Performed Task 1 Task 2 Data 2 Owl monkeys Belle,
Carmen 2 Rhesus monkeys Aurora, Ivy 54-192 sorted cells Cortices
sampled: PP, M1, PMd, S1, SMA Neuronal rate (100 Hz) and behavior
is time synchronized and downsampled to 10Hz
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100 msec Binned Counts Raster of 105 neurons (spike
sorted)
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Ensemble Correlations Local in Time are Averaged with Global
Models
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Computational Models of Neural Intent Three different levels of
neurophysiology realism Black Box models function relation between
input - desired response (no realism!) Generative Models state
space models using neuroscience elements (minimal realism). White
models significant realism (wish list!)
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Optimal Linear Model The Wiener (regression) solution
Normalized LMS with weight decay is a simple starting point. Four
multiplies, one divide and two adds per weight update Ten tap
embedding with 105 neurons For 1-D topology contains 1,050
parameters (3,150) Z -1 delay of 1 sample adder w i (n) parameter i
at time n w0w9w0w9
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3-D, 2-D Trajectory Modeling and Robot Control Collaboration
with Miguel Nicolelis, Duke University Sponsored by DARPA
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Time-Delay Neural Network (TDNN) The first layer is a bank of
linear filters followed by a nonlinearity. The number of delays to
span I second y(n)= wf(wx(n)) Trained with backpropagation Topology
contains a ten tap embedding and five hidden PEs 5,255 weights
(1-D) Principe, UF
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Multiple Switching Local Models Multiple adaptive filters that
compete to win the modeling of a signal segment. Structure is
trained all together with normalized LMS/weight decay Needs to be
adapted for input-output modeling. We selected 10 FIR experts of
order 10 (105 input channels) d(n)
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Recurrent Multilayer Perceptron (RMLP) Nonlinear Black Box
Spatially recurrent dynamical systems Memory is created by feeding
back the states of the hidden PEs. Feedback allows for continuous
representations on multiple timescales. If unfolded into a TDNN it
can be shown to be a universal mapper in R n Trained with
backpropagation through time
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Generative Models for BMIs Use partial information about the
physiological system, normally in the form of states. They can be
either applied to binned data or to spike trains directly. Here we
will only cover the spike train implementations. Difficulty of
spike train Analysis: Spike trains are point processes, i.e. all
the information is contained in the timing of events, not in the
amplitude of the signals!
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Particle Filters for Point Processes Kinematic State Neural
Tuning function spike trains Prediction Updating NonGaussian
P(state|observation) Linear filter nonlinearity f Poisson model
kinemati cs spikes Instantaneous tuning model
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Generative Data Modeling .. Neural Channels Time Observable
Processes (probed neurons) Hidden Processes (Brain areas)
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BMI lessons learned BMIs are beyond the Proof of Concept stage,
but. Present systems are signal translators and will not be the
blue print for clinical applications Current decoding methods use
kinematic training signals - not available in the paralyzed I/O
models cannot contend with new environments without retraining BMIs
should not be simply a passive decoder incorporate cognitive
abilities of the user
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BMI lessons learned BMIs are beyond the Proof of Concept stage,
but. Present systems are signal translators and will not be the
blue print for clinical applications Current decoding methods use
kinematic training signals - not available in the paralyzed I/O
models cannot contend with new environments without retraining BMIs
should not be simply a passive decoder incorporate cognitive
abilities of the user
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A Paradigm Shift for BMIs! During training the user actions
create a desired response to the DSP algorithm. During testing the
DSP algorithm creates an approximation to the desired response. DSP
algorithm Desired response Neural Signal Processing
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The control algorithm learns through reinforcement to achieve
common goals in the environment. Shared control with user to
enhance learning in multiple scenarios and acquire the net benefits
of behavioral, computational, and physiological strategies X
Control Algorithm Learning Algorithm Neural Signal Processing A
Paradigm Shift for BMIs!
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Construction of a New Framework How to capitalize on the
perception-action cycle? The brain is embodied and the body is
embedded Need to quantify Brain State at different time resolutions
Intelligent behavior arises from the actions of an individual
seeking to maximize received reward in a complex and changing
world. The BMI must engage and dialogue with the user: Exploits
better engineering knowledge Utilizes cognitive states Dissects
behavior top-down Exploits rewards Learns with use Propose
Reinforcement Learning to train the BMI. FUTURE PAST INTERNAL
REPRESENTATION EXTERNAL WORLD LIMBIC SYSTEM ORGANIZED PAST
EXPERIENCE PREDICTIVE MODELING DOES ACTION MEET FUTURE REALITY?
SENSORY STIMULUS Causality line Body line
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Reward Learning Involves a Dialogue Relation between the agent
and its environment. Environment: You are in state 14. You have 2
possible actions. Agent:I'll take action 2. Environment: You
received a reinforcement of 17.8 units. You are now in state 13.
You have 2 possible actions. Agent:I'll take action 1. repeat AGENT
ENVIRONMENT actions rewards states Goal Start
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CABMI involves TWO intelligent agents in a cooperative
dialogue!!! states ROBOT actions rewards RATS BRAIN environment
RATS BRAIN COMPUTER AGENT Users neuromodulation sets the value
function for the CA Both the CA and the user have the same reward
in 3D space
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Features of co-adaptive BMI Enables intelligent system design
in BMIs Both systems adapt in close loop in a very tight coupling
between brain activity and computer agent ( CA states are specified
by brain activity). User must incorporate the CA in its world (can
a rat learn this?) CA must decode brain activity for its value
function (can it model the signature of behavior?). In fact CABMI
is a symbiotic biological- computer hybrid system. 31
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Experiment workspace [top view] The user learns first to
associate levers with water reward in a training phase. In brain
control, it progressively associates the blue guide LED of the
robotic arm with the target lever LEDs. Only when the robot presses
the target lever it will get reward.
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Experiment workspace [top view]
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Experimental CABMI Paradigm Incorrect Target Correct Target
Starting Position Match LEDs Grid-space Match LEDs Rats Perspective
Water Reward Map workspace to grid Rat Robot Arm Left LeverRight
Lever 27 discrete actions 26 movements 1 stationary
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Experimental CABMI Paradigm CA rewards are defined in 3D at the
target lever positions. RL is used to train the CA in brain control
(tabula rasa, i.e. no a priori information). During brain control,
shaping of the reward field increases the level of difficulty
across multiple sections with an adjustable threshold target.
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Neuromodulation defines the States Sampling rate 24.4 kHz Hall,
Brain Research (1974) 32 channels Spike sorted data Bilateral
Premotor/motor Areas
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Performance metrics Performance metrics: 1.Percentage of trials
earning reward 2.Average control time required to reach a target 4
sessions were simulated using random action selection to estimate
chance performance for the CABMI in increasing difficulty
tasks.
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% trials earning reward time to achieve reward Performance in 4
tasks of increasing difficulty
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Closed-Loop RLBMI Non-functional levers Functional levers Robot
workspace in rat visual field of view. BLUE Robot GREEN - Lever
Top-view of the rat behavioral cage.
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It is well established that preparation, execution, and also
imagination of movement produce an event-related desynchronization
(ERD) over the sensorimotor areas, with maxima in the alpha band
(mu rhythm, 10 Hz) and beta band (20 Hz). The mu ERD is most
prominent over the contralateral sensorimotor areas during motor
preparation and extends bilaterally with movement initiation ERD
during hand motor imagery is very similar to the pre- movement ERD,
i.e., it is locally restricted to the contralateral sensorimotor
areas Event Related Desynchronization (ERD) and synchronization
(ERS)
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During movement preparation and execution, an increase of
synchronization (ERS) in the 10-Hz band normally appears over areas
not engaged in the task (idling) ERS can also be observed after the
movement, over the same areas that had displayed ERD earlier Event
Related Desynchronization (ERD) and synchronization (ERS)
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Beta rebound following movement and somatosensory stimulation
The general finding is that beta oscillations are desynchronized
during preparation, execution, and imagination of a motor act After
movement offset, the beta band activity recovers very fast (