New Method of Classifying EEG Signals in Brain-Computer Interfaces

Distinguishing the states of “movement” or “rest” in electroencephalogram (EEG) plays an important role in the domain of brain computer interface (BCI). According to the electroencephalogram feature, Hidden Markov model (HMM)-AR might be a useful tool in EEG pattern classification. The method which jointly employs Laplacian filter, ICA transform, and HMM-AR is presented for EEG pattern classification. The hybrid method is confirmed through the classification of EEG that is recorded during the imagination of a left or right hand movement. The results illustrate the algorithm can availably classify the two brain states of movement and rest. The algorithm for cue movement determination has been designed resulting in detecting the movements within one second interval. it prove the algorithm feasibility in BCI data sets.