Non-Invasive BCI

1929

Hans Berger – Discovered the EEG Electroencephalograph –

Signal Reflecting the electrical field produced by trillions of individual synaptic connections in the cortex and subcortical structures of the brain

EEG

EEG

EEG

Niels Birbaumer – Trained severely paralyzed people to self-regulate

the slow cortical potentials in their EEG in such a way that these signals could be used as a binary signal to control a computer cursor (1990s)

Tests included writing characters with the cursor System users require training just as any person is

trained to use a keyboard or a computer

Those who depend

ALS

Amyotrophic Lateral sclerosis –Muscle weakness and atrophy throughout the body

caused by the degeneration of upper and lower motor neurons.

Individuals may ultimately lose ability to initiate and control all voluntary movement

For the most part, cognitive function is preserved Sensory nerves and the autonomic nervous system

are generally unaffected

ALS

BCI systems have the ability to allow a paralyzed, “locked-in” patient to communicate words, letters and simple commands to a computer interface that recognizes different outputs of EEG signals and translates them through use of assigned algorithms into a specific function or computing output that the user has the ability to control.

A complex mechanical BCI system would allow a user to control an external system possibly an artificial limb by creating an output of specific EEG frequency

P300 Speller

User observes 6x6 matrix where each cell contains a character or symbol

User receives stimuli that coordinate with a specific output

User learns to recognize certain stimuli that exist in relation to a specific output

System created successful feedback when tested among the ALS population

EEG Rhythms

For analyzing EEG signals, studies suggest that frequencies of 8-12 Hz (mu) and 13-28 Hz (Beta) are most sensible for human control

The form or magnitude of a voltage change evoked by a stereotyped stimulus is known as an evoked potential and can serve as a command

ie. The amplitude of the EEG in a particular frequency band, can be used to control movement of a cursor on a computer screen

Non-Invasive BCI

Forefront of human experimentation

Cost effective

No implantation

Susceptible to noise

Cranial barrier dampens signal

What about right now

Today, BCIs are already being incorporated into modern technologically dependent society As they were once thought to be strictly

a bridge between a neurologically

disconnected brain to an outside mechanism

of replacing neuromuscular function,

the commercial exploitations have already

begun as devices can now be purchased that

allow users to control an exterior system

and navigate and control a graphical

Interface using only EEG output signals

NeuroSky

Developers at NeuroSky created the Brainwave, a comprehensive non-invasive BCI that connects the user to iOS and Android platforms, and transfers all signal information through Bluetooth as opposed to radio.

The EEG outputs for this setup are controlled primarily by variations in brain-state. In order to achieve a specific level of EEG the user may be prompted to relax or improve focus, thus altering the specific output of brain energy and ultimately changing the level of expressed EEG signals

Emotiv

Devolped a BCI called the EPOC

16 sensors capture EEGs to the extent of which the system can return feedback to let the user know whether or not they blinked, or sneezed, or smiled

The device allows a user to connect to a computer, and perform all basic functions that they otherwise would control using a keyboard, but with the mind. That includes control of gaming platforms as well

Future

For the future, BCI technology seems very applicable in a wide variety of areas whether it be medically or commercially

The possibilities of how far the systems can go is virtually limitless

Control of subvocalization and more advanced EEG processing could lead to telepathic communication and active learning mechanisms

This all would bring up an unfeasible amount of ethical discomfort and confrontation

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Naci , L., Cusack, R., Jia , V., & Owen, A. (2013). The brain's silent messenger: Using selective attention to decode human thought for brain-based communication . The Journal of Neuroscience , Retrieved from http://www.cusacklab.org/downloads/nacietal_jon2013.pdf

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Schalk, S., McFarland , D., Hinterberger, T., Birbaumer, N., & Wolpaw , J. (2004 ). Bci2000: A general-purpose brain-computer interface (bci) system . IEEE Transactions on Biomedical Engineering , 51(6), 1034-1043. Retrieved from http://bpv-tese.googlecode.com/hg-history/095dce5394352001ef2ddaefe6f10678ca6413d5/src/referencias/10.1.1.115.7600.pdf

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