EEG signal separation for multi-class motor imagery using common spatial patterns based on Joint Approximate Diagonalization

The design of multiclass BCI is a very challenging task because of the need to extract complex spatial and temporal patterns from noisy multidimensional time series generated from EEG measurements. This paper proposes a Multiclass Common Spatial Pattern (MCSP) based on Joint Approximate Diagonalization (JAD) for multiclass BCIs. The proposed method based on fast Frobenius diagonalization (FFDIAG) is compared with another method based on Jacobi angles on the BCI competition IV dataset 2a. The classification accuracies obtained from 10×10-fold cross-validations on the training dataset are compared using K-Nearest Neighbor, Classification Trees and Support Vector Machine classifiers. The proposed MCSP based on FFDIAG yields an averaged accuracy of 53.6% compared to 32.8% given by the method based on Jacobi angles and 27.8% of the one versus rest CSP methods.