BCI competition 2003-data set IV:An algorithm based on CSSD and FDA for classifying single-trial EEG

This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set of "BCI Competition 2003" with a classification accuracy of 84% on the test set.