Classifying EEG Signals Based HMM-AR

Whether "movement" or "rest" in Electroencephalogram (EEG) plays an important role in the domain of brain computer interface (BCI). Hidden Markov model (HMM)-AR might be a useful tool in EEG pattern classification since EEG data is a multivariate time series data which contains noise and artifacts. The methods are presented for EEG pattern classification which jointly employ Laplacian filter, ICA transform and HMM. Our hybrid method is confirmed through the classification of EEG that is recorded during the imagination of a left or right hand movement. The algorithm for cue movements determination has been designed resulting in detecting the movements within one second interval.