Mining multi-channel EEG for its information content: an ANN-based method for a brain-computer interface.

We have studied 56-channel electroencephalograms (EEG) from three subjects who planned and performed three kinds of movements, left and right index finger, and right foot movement. Using autoregressive modeling of EEG time series and artificial neural nets (ANN), we have developed a classifier that can tell which movement is performed from a segment of the EEG signal from a single trial. The classifier's rate of recognition of EEGs not seen before was 92-99% on the basis of a 1s segment per trial.