PCA-based linear dynamical systems for multichannel EEG classification

EEG-based brain computer interface (BCI) provides a new communication channel between human brain and computer. The classification of EEG data is an important task in EEG-based BCI. We present methods which jointly employ principal component analysis (PCA) and linear dynamical system (LDS) modeling for the task of EEG classification. Experimental study for the classification of EEG data during imagination of a left or right hand movement confirms the validity of our proposed methods.