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
If you were to cut the brain in half  top and bottom

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

9. 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

11. 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

14. EEG Rhythms
For analyzing EEG signals, studies suggest that frequencies of 8-12 Hz (mu) and 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

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

17. 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

18. 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

20. 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

22. 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

23. 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
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 "Brain–computer Interface." Wikipedia. Wikimedia Foundation, 21 Sept Web. 23 Sept
Sellers , E. (2013 ). New horizons in brain computer interface research . U.S national library of medicine, Retrieved from
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
Wolpaw , J., McFarland , D., & Vaughan, T. (2000). Brain-computer interface research at the wadsworth center . IEEE Transaction on Rehabilitation Engineering , 8(2), Retrieved from
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), Retrieved from history/095dce ef2ddaefe6f10678ca6413d5/src/referencias/ 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), Retrieved from Overview.pdf