Complex sparse spatial filter for decoding mixed frequency and phase coded steady-state visually evoked potentials.

BACKGROUND: Mixed frequency and phase coding (FPC) can achieve the significant increase of the number of commands in steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI). However, the inconsistent phases of the SSVEP over channels in a trial and the existence of non-contributing channels due to noise effects can decrease accurate detection of stimulus frequency.

NEW METHOD: We propose a novel command detection method based on a complex sparse spatial filter (CSSF) by solving ℓ1 - and ℓ2,1 -regularization problems for a mixed-coded SSVEP-BCI. In particular, ℓ2,1 -regularization (aka group sparsification) can lead to the rejection of electrodes that are not contributing to the SSVEP detection.

RESULTS: A calibration data based canonical correlation analysis (CCA) and CSSF with ℓ1 - and ℓ2,1 -regularization cases were demonstrated for a 16-target stimuli with eleven subjects. The results of statistical test suggest that the proposed method with ℓ1 - and ℓ2,1 -regularization significantly achieved the highest ITR.

COMPARISON WITH EXISTING METHODS: The proposed approaches do not need any reference signals, automatically select prominent channels, and reduce the computational cost compared to the other mixed frequency-phase coding (FPC)-based BCIs.

CONCLUSIONS: The experimental results suggested that the proposed method can be usable implementing BCI effectively with reduce visual fatigue.