Hidden Markov models for online classification of single trial EEG data

Abstract 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.

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