The Strathclyde brain computer interface.

Brain-computer interfaces (BCI) offer potential for individuals with a variety of motor and sensory disabilities to control their environment, communicate, and control mobility aids. However, the key to BCI usability rests in being able to extract relevant time varying signals that can be classified into usable commands in real time. This paper reports the first success of the Strathclyde BCI controlling a wheelchair on-line in Virtual Reality. Surface EEG recorded during wrist movement in two different directions were classified and used to control a wheelchair within a virtual reality environment. While Principal Component Analysis was used for feature vector quantiser distances were used for classification. Classification success rates between 68% and 77% were obtained using these relatively simple methods.