Spatial fusion of maximum signal fraction analysis for frequency recognition in SSVEP-based BCI. (August 2020)

• Record Type: Journal Article
• Authors: Li, Zhenhua; Liu, Ke; Deng, Xin; Wang, Guoyin
• Is Part Of: Biomedical signal processing and control. Volume 61(2020)
• Publication Date: 2020-08
• Subjects: Brain-computer interface (BCI) -- Electroencephalogram (EEG) -- Steady-state visual evoked potential (SSVEP) -- Maximum signal fraction analysis (MSFA) -- Spatial dimension fusion; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102042
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A SSVEP frequency recognition method, namely FoMSFA, is proposed for reliable target identification in short time windows. FoMSFA utilizes both spatial and frequency dimension fusion strategy to effectively use the information of all spatial filters by MSFA in all sub-bands. Validations on public and our laboratory dataset indicate the promising potential of FoMSFA to implement a high-performance SSVEP-based BCI system. Abstract: Maximum signal fraction analysis (MSFA) is an efficient method for frequency identification in steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI). However, standard MSFA only utilizes the spatial filter corresponding to the maximum eigenvalue for multichannel EEG signals de-noising and frequency identification, and discards other weight vectors containing discriminant information. In this work, we proposed a new SSVEP frequency recognition method with spatial dimension fusion strategy, i.e., FoMSFA, which employs the information of all spatial filters from standard MSFA, and uses a nonlinear weighting function to fuse multiple sets of correlation coefficients to identify the frequency of SSVEP signal. Numerical results of the benchmark dataset with 35 subjects and our laboratory dataset with 10 subjects show that FoMSFA outperforms the standard MSFA and CCA-based methods. The proposed method has the potential to design high-performance SSVEP-based BCI systems for communication and control.