A joint tensor diagonalization approach to active data selection for EEG classification

We present a novel method based on joint tensor diagonalization for selecting or weighting electroencephalogram (EEG) data to estimate the covariance matrices to accurately find common spatial pattern (CSP). CSP and its variants need a pair of covariance matrices of two different tasks, which are obtained as the average over trials. This trial average can affect the accurate estimation of covariance matrices and cause the decrease of classification accuracy in brain machine interfaces (BMIs) due to the non-stationarity of EEG or experimental environments. We focus on the fact that finding CSP is equivalent to joint diagonalization of a pair of covariance matrices, and extend it to joint diagonalization of data tensor at each trial to determine importance of each trial. Numerical experiment of motor imagery (MI) classification supports the proposed algorithm is effective.