SSVEP signal classification and recognition based on individual signal mixing template multivariate synchronization index algorithm. (February 2021)

• Record Type: Journal Article
• Authors: Qin, Ke; Wang, Raofen
• Is Part Of: Biomedical signal processing and control. Volume 64(2021)
• Publication Date: 2021-02
• Subjects: Brain-computer interface -- SSVEP signal -- Multivariate synchronization index algorithm -- Personal training template -- Harmonic sensitivity coefficient; 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.2020.102304
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Individual signal mixing template MSI (IST-MSI) algorithm was proposed in this paper. IST-MSI algorithm incorporates individual SSVEP training data into the reference signal IST-MSI algorithm takes the personal harmonic sensitivity into the SSVEP identification process. CCA method is used to reduce the redundant information in individual templates. The proposed R3 reference signal achieves about 5.7% accuracy improvement. Abstract: With the development of automation technology, Brain Computer Interface (BCI) has been increasingly integrated into people's daily life, among which Steady State Visual Evoked Potential (SSVEP) has attracted much attention due to its high signal-to-noise ratio (SNR) and wide application scenarios. To improve the classification accuracy of SSVEP signals, a novel individual signal mixing template multivariate synchronization index algorithm (IST-MSI) was proposed in this paper, which incorporated individual training template and individual harmonic sensitivity coefficient into the standard MSI algorithm. Specifically, the proposed method first enlarged the frequency-domain power spectrum of the fundamental frequency and its harmonics to reduce the redundant information in the individual training template. The synchronization index values at non-target frequency identified by MSI algorithm are significantly reduced through unequal ratio scaling of harmonic sensitivity coefficient, thereby improving the SSVEP recognition. The experimental results showed that under the signal length of 1.2 s, the average classification accuracy of IST-MSI algorithm reached 84.3 % in six target frequencies, which was 5.8 % higher than that of standard MSI algorithm. This study confirmed the efficacy of the proposed IST-MSI algorithm for SSVEP recognition, demonstrating its promise in developing an improved BCI system.