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

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.

journal_name

J Neurosci Methods

journal_title

Journal of neuroscience methods

authors

Morikawa N,Tanaka T,Islam MR

doi

10.1016/j.jneumeth.2018.04.001

subject

Has Abstract

pub_date

2018-07-01 00:00:00

pages

1-10

eissn

0165-0270

issn

1872-678X

pii

S0165-0270(18)30098-0

journal_volume

304

pub_type

杂志文章

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