Electrocorticography (ECoG) signals have been proved to be associated with different types of motor imagery and have used in brain-computer interface (BCI) research. This paper studies the channel selection and feature extraction using band powers (BP) for a typical ECoG-based BCI system. The subject images movement of left finger or tongue. Firstly, BP features were used for channel selection, and 11 channels which had distinctive features were selected from 64 channels. Then, the features of ECoG signals were extracted using BP, and the dimension of feature vector was reduced with principal components analysis (PCA). Finally, Fisher linear discriminant analysis (LDA) was used for classification. The results of the experiment showed that this algorithm has got good classification accuracy for the test data set.
Brain–computer interface channel selection optimization using meta-heuristics and evolutionary algorithms
