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AR-PCA-HMM Approach for Sensorimotor Task Classification in EEG-based Brain-Computer Interfaces
Authors: Ali Ozgur Argunsah, Müjdat Çetin
Published in: International Conference on Pattern Recognition, Istanbul, Turkey, August 2010
Publication year: 2010
Abstract: 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.
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