One Class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI)

摘要

The goal of a Brain-Computer Interface (BCI) is to enable communication by pure brain activity without the need for muscle control. Recently BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present two new methods to improve classification in a c-VEP BCI. Canonical correlation analysis can be used to build an optimal spatial filter for detection of c-VEPs, while the use of a one class support vector machine (OCSVM) makes the BCI more robust in terms of artefacts and thus increases performance. We show both methods to increase performance in an offline analysis on data from 8 subjects. As a proof of concept both methods are tested online with one subject, who achieved an average performance of 133 bit/mih, which is higher than any other bitrate reported so far for a non-invasive BCI.