AR-PCA-HMM Approach for Sensorimotor Task Classification in EEG-based Brain-Computer Interfaces
Ali Ozgur Argunsah, Müjdat Çetin
International Conference on Pattern Recognition, Istanbul, Turkey, August 2010
We propose an approach based on Hidden Markov models (HMMs) combined with principal component analysis (PCA) for classiﬁcation of four-class single trial motor imagery EEG data for brain computer interfacing (BCI) purposes. We extract autoregressive (AR) parameters from EEG data and use PCA to decrease the number of features for better training of HMMs. We present experimental results demonstrating the improvements provided by our approach over an existing HMM-based EEG single trial classiﬁcation approach as well as over state-of-the-art classiﬁcation methods.