Sparse spatial filtering in frequency domain of multi-channel EEG for frequency and phase detection

A brain-computer interface (BCI) based on steady state visual evoked potentials (SSVEPs) is one of the most practical BCI, because of high recognition accuracies and short time training. To increase the number of commands of SSVEP-based BCI, recently a frequency and phase mixed-coded SSVEP BCI has been proposed. However, in order to detect frequency and phase of SSVEPs accurately, it is required to treat multi-channel phases to select useful channels for detecting commands. In this paper, we propose a novel method for estimating both frequency and phase of SSVEPs with sparse complex spatial filters. We conducted experiments for evaluating the performance of the proposed method in a mixed-coded SSVEP based BCI. As a result, the proposed method showed higher recognition accuracies and lower calculation cost of command detection than conventional methods. Moreover, the proposed method achieved automatic channel selection.