EEG Feature Extraction And Online BCI Research

Brain-Computer Interface is a new way of human-computer interface.It is notdependent on the brain's normal output channels (peripheral nerves and muscles),butdirectly change the information between the brain and outside world.As amultidisciplinary cross technology, BCI relate to Biomedical Engineering, RehabilitationEngineering, Electronics Engineering and Artificial Intelligence.BCI system can explainthe brain' mental task, it is vary important to get the relationship between brain's sourceand mental task, The theory research such as feature extraction, classification andexperiment research of electroencephalogram(EEG) play important roles.EEG signal can be collected conveniently, so it can be easily popularize. The maincontributions of the paper are as follows:First of all, a new method which combines GSVD and kernel method is proposed tosolve the problems of small sample size in traditional LDA and kernel LDA. In order torelax the presumption of strictly linear pattern and relieve the nonsingular, GSVD is usedto solve the optimizing spatial filter.Secondly, for the small sample size, this paper use another method calledDiscriminative Common Vector. In BCI system, the number of sample of EEG signal islow than the sample space dimension, i.e. small sample size problem. DCV removes allthe features that are in the direction of the eigenvectors corresponding to the nonzeroeigenvalues of the scatter matrix. The DCV can be the feature to be classified.At last, the online BCI is involve. A BCI car controlled system based on P300potential is realized. We makes a deep research on P300potential. With the BCI2000platform, an online visual stimulation BCI external equipment control system is realized.The research subjects can control the car by visual stimulation after a short train time.