Independent component analysis-based channel selection to achieve high performance of N200 and P300 classification

This paper proposes a method for achieving a high performance of N200 and P300 classification, which applies independent component analysis (ICA) to select the channels whose brain signals contain large N200 and P300 potentials and small artifacts as the optimal channels to extract the features. The study results show that our method achieves an average accuracy of 99.3% over 4 subjects.