Spatial Filter Selection with LASSO for EEG Classification

Abstract

Spatial filtering is an important step of preprocessing for electroencephalogram (EEG) signals. Extreme energy ratio (EER) is a recently proposed method to learn spatial filters for EEG classification. It selects several eigenvectors from top and end of the eigenvalue spectrum resulting from a spectral decomposition to construct a group of spatial filters as a filter bank. However, that strategy has some limitations and the spatial filters in the group are often selected improperly. Therefore the energy features filtered by the filter bank do not contain enough discriminative information or severely overfit on small training samples. This paper utilize one of the penalized feature selection strategies called LASSO to aid us to construct the spatial filter bank termed LASSO spatial filter bank. It can learn a better selection of the spatial filters. Then two different classification methods are presented to evaluate our LASSO spatial filter bank. Their excellent performances demonstrate the stronger generalization ability of the LASSO spatial filter bank, as shown by the experimental results.