Greedy solutions for the construction of sparse spatial and spatio-spectral filters in brain computer interface applications

In the original formulation of common spatial pattern (CSP), all recording channels are combined when extracting the variance as input features for a brain computer interface (BCI). This results in overfitting and robustness problems of the constructed system. Here, we introduce a sparse CSP method in which only a subset of all available channels is linearly combined when extracting the features, resulting in improved generalization in classification. We propose a greedy search based generalized eigenvalue decomposition approach for identifying multiple sparse eigenvectors to compute the spatial projections. We evaluate the performance of the proposed sparse CSP method in binary classification problems using electrocorticogram (ECoG) and electroencephalogram (EEG) datasets of brain computer interface competition 2005. We show that the results obtained by sparse CSP outperform those obtained by traditional (non-sparse) CSP. When averaged over five subjects in the EEG dataset, the classification error is 12.3% with average sparseness level of 11.6 compared to 18.4% error obtained by the traditional CSP with 118 channels. The classification error is 10% with sparseness level of 7 compared to that of 13% obtained by the traditional CSP using 64 channels in the ECoG dataset. Furthermore, we explored the effectiveness of the proposed sparse methods for extracting sparse common spatio-spectral patterns (CSSP).