ECoG Based Brain Computer Interface with Subset Selection

Abstract

We describe an adaptive approach for the classification of multichannel neural recordings for a brain computer interface. A dual-tree undecimated wavelet packet transform generates a structured redundant feature dictionary with different time-frequency resolutions computed on multichannel neural recordings. Rather than evaluating the individual discrimination performance of each electrode or candidate feature, the proposed approach implements a wrapper strategy combined with pruning to select a subset of features from the structured dictionary by evaluating the classification performance of their combination. The pruning stage and wrapper combination enables the algorithm to select a subset of the most informative features coming from different cortical areas and/or time frequency locations with faster speeds, while guaranteeing high generalization capability and lower error rates. We show experimental classification results on the ECoG data set of BCI competition 2005. The proposed approach achieved a classification accuracy of 93% by using only three features. This is a marked improvement over other reported approaches that use all electrodes or require manual selection of sensor subsets and feature indices and at best achieve slightly lower classification accuracies.