An Automatic SSVEP Component Selection Measure for High-Performance Brain-Computer Interface

This paper proposed an automatic steady-state visual evoked potential (SSVEP) component selection (SCS) measure for a high-performance SSVEP-based brain-computer interface (SBCI) system. First, multi-electrode raw electroencephalogram signals are spatially pre-processed using a blind source separation technique resulting in multi-source components. The SCS measure of each component is then calculated by continuous wavelet transform (CWT), and the ensemble features that contain the weighted CWT energy of individual SSVEP harmonic are extracted. Second, the SSVEP component with maximal SCS measure is considered to have the highest signal-to-noise ratio. In our SBCI system, six stimulus frequencies served as the input patterns. Offline analyses were performed, through which the common electrode locations, the time window size, and the number of harmonics were defined. Thereafter the results of our method were compared with those of others. We next carried out an online test of the SBCI for 11 subjects using eight common electrode locations, a 1.5-s time window, and the first and second harmonics. The test results showed that our method achieved an average accuracy of 95.2 % and a practical bit rate of 68.2 bits/min.