Sparse spatial filter optimization for EEG channel reduction in brain-computer interface

Abstract

Spatial filters are useful in discriminating different classes of electroencephalogram (EEG) signals such as those corresponding to motor activities. In the case of discriminating two classes of signals, EEG signals are projected onto a space where one class of signals is maximally scattered and the other is minimally scattered. This paper finds a minimal number of electrodes that can achieve the discrimination. Applying many electrodes is tedious and time-consuming. To reduce the number of electrodes, we propose inducing sparsity in the spatial filter. We reformulate the optimization problem in Common Spatial Patterns by introducing an ^i-norm regularization term. Experimental results on five subjects show that the proposed method significantly reduces the number of electrodes while generating features with good discriminatory information. The number of electrodes on average, is reduced to 11% (of the 118 electrodes) while the average drop in the classification accuracy is only 3.8%.