Automatic differentiation of multichannel EEG signals

Intention of movement of left or right index finger, or right foot is recognized in electroencephalograms (EEGs) from three subjects. We present a multichannel classification method that uses a committee of artificial neural networks to do this. The classification method automatically finds spatial regions on the skull relevant for the classification task. Depending on subject, correct recognition of intended movement was achieved in 75%-98% of trials not seen previously by the committee, on the basis of single EEGs of one-second duration. Frequency filtering did not improve recognition. Classification was optimal during the actual movement, but a first peak in the classification success rate was observed in all subjects already when they had been cued which movement later to perform.