A brain-computer interface algorithm based on Hidden Markov models and dimensionality reduction

We consider the problem of motor imagery EEG data classification within the context of brain-computer interfaces. We propose an approach based on Hidden Markov models (HMMs). Our approach is different from existing HMM-based techniques in that it uses features based on autoregressive parameters together with dimensionality reduction based on principal component analysis (PCA). We demonstrate the effectiveness of our approach through experimental results for two and four-class problems based on a public dataset, as well as data collected in our laboratory.