Design of a novel covert SSVEP-based BCI

Brain computer interfaces (BCI) employing steady-state visually evoked potential (SSVEP) modulations have been investigated increasingly in the last years because of their high signalto-noise ratio and information transfer rate. However, independent SSVEP BCI based on covert attention show a drop in robustness which makes it difficult to use on patients with impaired or nonexistent ocular motor control. In the present paper, offline analysis is aimed at investigating the influence of three important parameters on the performance of covert SSVEP BCI : feature extraction algorithms, window length and number of harmonics. We also proposed a new ”checkerboard” pattern and compared its performance with lines pattern. We have shown that the use of this pattern and only one harmonic yielded an average accuracy of approximately 79% across five subjects (with four subjects at more than 81%) with 6s-window length and feature extraction algorithm based on canonical correlation analysis or lock-in analyzer system. The short 5 or 6s-concentration time, the absence of training due to the use of only one harmonic, the robustness make this method very well suited for detecting command following and testing communication in unresponsive post-comatose patients.