The following is the excerpt from "Brainy Communicator allows people to exchange information directly with computers" (Guan Cuntai), Innovation: The Magazine of Research & Technology, June, 2005. "Locked-in" people, those with complete paralysis, require BCI as a communication device immediately. Niels Birbaumer and his group at Eberhard-Karls-University of Tübingen, Germany, developed a spelling system called the Thought-Translation Device (TTD) to help patients with amyotrophic lateral sclerosis (also known as Lou Gehrig’s disease) to communicate. A professional who has lived on a respirator for the past several years could successfully write a letter with TTD [2]
Brainwaves consist of several rhythms owing to their frequency components. Among various rhythms, an individual can voluntarily regulate his or her µ and ß rhythms, which revolve around 10Hz and 20Hz, respectively. Relying on this phenomenon, Jonathan Wolpaw and his team at the Wadsworth Center of the New York State Department of Health have been working on developing BCI systems for a two-dimensional cursor control that can provide disabled people, such as those with spinal-cord injuries, to control robotic arms or prostheses [3].
Neurophysiologists can consistently map motor-nerve systems to specific brain areas. For instance, hand movement would result in a compression of µ or ß rhythms at the counter lateral hemisphere of the vertex. Gerts Pfurtscheller and his colleagues at Graz University of Technology, Austria, developed various BCIs based on the detection of so-called event related rhythm synchronization and desynchronization by a subject’s imaging limb movement [4].
The advantage of motor imagery BCI, compared to self-regulation of slow-cortical potential or µ/ß rhythms, is that users can quickly master it as it is relatively intuitive. A challenge arises, however, when researchers try to recognize correctly what action users are attempting by reading their EEG data because of low noise-to-signal ratio. Klaus-Robert Muller and his Fraunhofer FIRST team in Germany successfully applied machine-learning methods to BCI by letting the machine learn instead of the person [5].
Another important brain signal BCI research widely studies and deploys is P300 potential. When an anticipatory or a rare event becomes interspersed with frequent events, it creates a measurable difference at the central or parietal sites of the vertex. This positive potential typically occurs about 300 milliseconds after the event occurs. Lawrence Farwell and Emanuel Donchin proposed the first P300 BCI; Donchin and others later improved its performance and built a prosthesis based on it. Recent research showed that, with proper design, a P300-based BCI could run 1.5 bits per second, the highest information-transfer rate in all BCIs to date [6].
With the progress of Brain-Computer Interface technology, a wide array of potential applications can be developed to meet the needs of healthcare, multimedia communication, and so on. Here are some examples of BCI applications [1]:
- Provide disabled people with communication, environment control, and movement restoration;
- Provide enhanced control of devices such as wheelchairs, vehicles, or assistance robots for people with disabilities;
- Provide additional channel of control in computer games;
- Monitor attention in long-distance drivers or aircraft pilots, send out alert and warning for aircraft pilots;
- Develop intelligent relaxation devices;
- Control robots that function in dangerous or inhospitable situations (e.g., underwater or in extreme heat or cold);
- Create a feedback loop to enhance the benefits of certain therapeutic methods;
- Develop passive devices for monitoring function, such as, monitoring long-term drug effects, predicting seizures, evaluating psychological state, etc.;
- Monitor sleep stage and help diagnose.
It is anticipated that the coming years are likely to bring major breakthroughs in brain-computer-interface research and development due to the extensive international research activities and collaborations, and the continuing trend of Moore's law.
References
[1] Wolpaw J R, Birbaumer N, MacFarland D J, Pfurtscheller G and Vaughan T M, "Brain-computer interface for communication and control," Clin. Neurophyiol., 113:767-791, 2002.
[2] Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, Perelmouter J, Taub E, and Flor H, "A spelling device for the paralysed," Nature , vol 398, pp297-98, March, 1999.
[3] Wolpaw J and McFarland D, "Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans," Proceedings of National Academy of Sciences , vol 101, no. 51, pp 17849-17854, Dec, 2004,
[4] Pfurtscheller G and Neuper C, "Motor imagery and direct brain–computer communication," Proc. IEEE , vol 89 , no7, pp1123–34, July, 2001.
[5] Dornhege G, Blankertz B, Curio G, and Müller K-R . "Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms," IEEE Trans on Biomedical Engineering , vol 51, no 6, pp993-1002, June, 2004.
[6] Donchin E, Spencer K M and Wijesinghe R, "The mental prosthesis: assessing the speed of a P300-based brain–computer interface," IEEE Trans. Rehabilitation Engineering, vol 8, no 2, pp174–179, June, 2000.
[7] IEEE Transaction on Biomedical Engineering, June, 2004, Special Issue on Brain-machine Interface.
[8] IEEE Transactions on Neural Systems and Rehabilitation Engineering, June, 2003, Special Issues on Brain-Computer Interface.
