An Unsupervised Channel-Selection Method for SSVEP-based BCI Systems

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Abstract
Brain-computer interface (BCI)-based systems have been successfully implemented as a communication tool for people with motor deficits that obstruct normal communication. The accuracy of the algorithms used for determining user-selected commands directly impact their practicality to the user. These algorithms are divided into two principal categories, supervised and unsupervised. While the former achieves higher accuracy, the latter is useful when training is not practical for the user.In this paper, we introduce an unsupervised algorithm for steady-state visual evoked potential (SSVEP)-based BCIs, which works in three steps: (i) it selects multiple sets of EEG (electroencephalogram) channels, followed by (ii) existing feature extraction methods applied to each one of these channel sets. As its final step, (iii) it combines the extracted features from these channel sets by performing a majority vote, yielding a classification. We evaluate the information transfer rate (ITR) attained using our proposed method on a database of 35 subjects using three different (CCA, FBCCA, MSI) feature extraction methods in step (ii). We compare these results to existing methods in the literature that use a single channel set without a majority vote. The proposed method indicates an improvement in at least 7 subjects using any of the three feature extraction methods.

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