In this paper, we propose a novel optimal spatio-temporal filter, termed local temporal common spatial patterns (LTCSP), for robust single-trial elctroencephalogram (EEG) classification. Different from classical common spatial patterns (CSP) that uses only global spatial covariances to compute the optimal filter, LTCSP considers temporally local information in the variance modelling. The underlying manifold variances of EEG signals contain more discriminative information. LTCSP is an extension to CSP in the sense that CSP can be derived from LTCSP under a special case. By constructing an adjacency matrix, LTCSP is formulated as an eigenvalue problem. So, LTCSP is computationally as straightforward as CSP. However, LTCSP has better discrimination ability than CSP and is much more robust. Simulated experiment and real EEG classification demonstrate the effectiveness of the proposed LTCSP method.
Algorithms Artificial Intelligence