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Maximum Signal Fraction Analysis for Enhancing Signal-to-Noise Ratio of EEG Signals in SSVEP-Based BCIs

2019-07-29

Authors: Wei, QG; Zhu, S; Wang, YJ; Gao, XR; Guo, H; Wu, X

IEEE ACCESS

Volume: 7 Pages: 85452-85461 Published: 2019 Language: English Document type: Article

DOI: 10.1109/ACCESS.2019.2925078

Abstract:

Various improved canonical correlation analysis (CCA) methods were developed for enhancing the

performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs).

Among them, the method combining CCA spatial filters from sine-cosine references and individual

templates yielded the highest performance. However, the CCA aims to optimize the correlation between two

sets of variables rather than the signal-to-noise ratio (SNR) of the SSVEP signals, upon which the

performance of an SSVEP-based BCI depends mainly. In this paper, a novel algorithm, namely, maximum

signal fraction analysis (MSFA), is proposed for creating spatial filters based on individual training

data. The spatial filter for a specific stimulus target is estimated by directly maximizing the averaged

SNR of the observed signals across multiple trials. An individual template is calculated for each target

by averaging training signals of multiple trials. Target recognition is based on template matching

between filtered template signals and a single-trial testing signal. Classification performance of the

MSFA-based method was evaluated on a benchmark dataset and compared with that of the CCA-based methods.

The results suggest that the proposed MSFA method significantly outperforms the CCA-based methods in

terms of classification accuracy, and thus, it has great potential to be applied in the real-life

SSVEP-based BCI systems.

Full Text:



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