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Non-Invasive BCI

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 –

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Non-Invasive BCI

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  1. Non-Invasive BCI

  2. 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

  3. EEG

  4. EEG

  5. 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

  6. Those who depend

  7. 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

  8. 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

  9. 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

  10. 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

  11. Non-Invasive BCI • Forefront of human experimentation • Cost effective • No implantation • Susceptible to noise • Cranial barrier dampens signal

  12. 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

  13. 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

  14. 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

  15. 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

  16. Bibliography • Curran , E., & Stokes , M. (2002). Learning to control brain activity: A review of the production and control of eeg components for driving brain-computer interface systems . Academic Press , Retrieved from http://hossein69.persiangig.com/.uZ900jjmWN/sdarticle.pdf • Wikipedia: Biomedical Engineering <en.wikipedia.org/wiki/ Biomedical_engineering>. • "Disruptions: Brain Computer Interfaces Inch Closer to Mainstream." Bits Disruptions Brain Computer Interfaces Inch Closer to Mainstream Comments. N.p., n.d. Web. 23 Sept. 2013."Brain–computer Interface." Wikipedia. Wikimedia Foundation, 21 Sept. 2013. Web. 23 Sept. 2013. • Sellers , E. (2013 ). New horizons in brain computer interface research . U.S national library of medicine, Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3658460/ • 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 • Wolpaw , J., McFarland , D., & Vaughan, T. (2000). Brain-computer interface research at the wadsworth center . IEEE Transaction on Rehabilitation Engineering , 8(2), 222-226. Retrieved from http://www.cs.hmc.edu/~keller/eeg/Wolpaw.pdf • 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 • Heetderks , W., McFarland , D., Hinterberger, T., Birbaumer, N., Wolpaw , J., Peckham, P., Donchin, E., & Quatrano, L. (2000). Brain-computer interface technology: A review of the first international meeting . IEEE Transactions on Rehabilitation Engineering , 8(2), 164-173. Retrieved from http://www.ocf.berkeley.edu/~anandk/neuro/BCI Overview.pdf

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