Brain Computer Interface Technology Based On Wavelet Transform

A brain-computer interface(BCI)is a communication system that does not depend on the brain's normal output pathways of peripheral nerves and muscles.Current interests in BCI development come mainly from the hope that this technology could be a new valuable augmentative communication option for those with severe notor disabilities that prevent them from using conventional augmentative technologies.BCI technology also has potential applications in other fields such as industry,space and defense.Wavelet transform has been studied here to extract the SCP signal from strong noises.A wavelet time-frequency filter was designed according to different time-frequency characteristics of SCP and noises.Combining with averaging method,the wavelet filter could improve signal noise ratio effectively.Pattern recognize is a important part of BCI system which is the main content of this paper .Pattern recognize including two parts are feature extraction and classification.Three methds for extracting featurevectors were developed.They are(1) Feature extracting vectors based on wavelet transform;(2) Feature extracting vectors based on AR-model;(3) Feature extracting vectors based on K-L transform;Meanwhile ,classification by linear discriminant Fisher criteria for extracting feature vectors.Experiments showed that the feature vector extracting from the wavelet transform domain and the SCP recognition algorithm based on fisher criteria are very effective and easy to be fulfilled.These algorithms are suitable for the research work.In conclusion,the method of using the SCP for BCI application is feasible.The signal extraction and the recognition algorithms are suitable for SCP based BCI applications. The research work has established a good base for future brain-computer interface implementations.