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. 2018 Jul 1:304:1-10.
doi: 10.1016/j.jneumeth.2018.04.001. Epub 2018 Apr 11.

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

Naoki Morikawa  1 , Toshihisa Tanaka  2 , Md Rabiul Islam  1
Affiliations

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

Naoki Morikawa et al. J Neurosci Methods. .

Abstract

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.

Keywords: Brain–computer interfaces (BCIs); Complex sparse spatial filter (CSSF); Electroencephalogram (EEG); Steady-state visually evoked potentials (SSVEPs).

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