A multi-class pattern recognition method for motor imagery EEG data

The Common Spatial Patterns (CSP) algorithm is useful for calculating spatial filters for detecting event-related desynchronization (ERD) for use in ERD-based brain-computer interfaces (BCIs). However, basic CSP is a supervised algorithm suited only to two-class discrimination; it is unable to solve multiclass discrimination problems. This paper proposes a new method named the binary common spatial patterns (BCSP) algorithm to extend the basic CSP method to multi-class recognition. Our method arranges the spatial filters and Fisher classifiers in the form of a binary tree whereby N - 1 spatial filters and N - 1 Fisher classifiers are calculated for N class recognition. This is fewer than must be calculated in other methods (e.g. one-versus-rest, OVR). This makes the overall classification procedure less redundant. Simulation results show that BCSP has better performance than the OVR scheme and outperforms the three best teams in the 2008 BCI-competition.

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