Classification of Brain-Computer Interfaces Using ICA+CSSD

Identification and classification technology plays an important part in study of the brain-computer interface (BCI) system. In this paper, an algorithm is presented to deals with the complex brain signals and extract features and classify single-trial electroencephalogram (EEG). The algorithm combines independent component analysis algorithm and common spatial subspace decomposition with support vector machine to extract features from multi-channel EEG and electro cortico gram (ECoG). This algorithm was applied to the results of data analysis show that the proposed method can classify with high accuracy.