Extracting nonlinear correlation for the classification of single-trial EEG in a finger movement task

The famous common spatial patterns (CSP) algorithm has shown to be useful for event-related desynchronization (ERD) feature extraction of multi-channel electroencephalogram (EEG) signals. Actually, CSP only extracts the linear correlation between each pair of channels. The performance of CSP severely depends on the preprocessing. Moreover, CSP and the subsequent classifier are not optimized by the same criteria. In this paper, we investigated the nonlinear correlation between channels with kernel technique, and proposed a unified prediction framework based on linear ridge regression model. This prediction framework integrates preprocessing, feature extraction and classification, can automatically select the time windows, frequency bands and regularization parameter by minimizing leave-one-out cross-validation error through gradient descent. Experimental results on the dataset IV, BCI competition II show the effectiveness of our approach.