A
brain-computer interface (BCI) is a system for direct communication between
brain and computer. A BCI can serve as a substitute for the normal human
communication channel, especially for individuals that suffer from severe motor
disabilities, as it relys solely on the brain’s activity and do not require
motor ability. The system exploits event-related brain potentials (ERP),
natural responses of the brain to external visual stimuli, as a medium for
communication. In this work independent component analysis (ICA) was used in
order to separate the ERP source from the background noise. A matched filter
was used together with averaging techniques for detecting the existence of
ERPs. The BCI developed in this work allows a subject to communicate one of 36
symbols. A further enhancement of the BCI allows it to adapt the communication
rate to the users’ ability. The processing method was evaluated offline on data
recorded from six healthy subjects. The method achieved a communication rate of
5.45 symbols/min with an accuracy of 92.1%, a communication rate equal to 23.75
bits/min (the communication rate takes error rates into account). The on-line
interface was tested with the same six subjects. The average communication rate
achieved was 4.5 symbols/min with an accuracy of 79.5%, a communication rate
equal to 15.43 bits/min. This research presents a working, simple, and
easy-to-use brain-computer interface. Data acquisition is performed by
noninvasive means with only a few scalp electrodes. The interface works
on-line, adapting the communication rate to the users’ performance to achieve
good results. The presented BCI achieves good performance compared to other
existing BCIs, and allows a reasonable communication rate, while maintaining a
low error rate.
