Hidden Markov models for online classification of single trial EEG data

Hidden Markov models (HMMs) are presented for the online classification of single trial EEG data during imagination of a left or right hand movement. The classification shows an improvement of the online experiment and the temporal determination of minimal classification error compared to linear classification methods. (C) 2001 Elsevier Science B.V. All rights reserved.