A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients

Pengfei Ma; Chaoyi Dong; Ruijing Lin; Shuang Ma; Tingting Jia; Xiaoyan Chen; Zhiyun Xiao; Yongsheng Qi

doi:10.1016/j.jneumeth.2022.109502

A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients

J Neurosci Methods. 2022 Apr 1:371:109502. doi: 10.1016/j.jneumeth.2022.109502. Epub 2022 Feb 11.

Authors

Pengfei Ma¹, Chaoyi Dong², Ruijing Lin¹, Shuang Ma¹, Tingting Jia¹, Xiaoyan Chen¹, Zhiyun Xiao¹, Yongsheng Qi¹

Affiliations

¹ College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China; Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Inner Mongolia, Hohhot 010051, China.
² College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China; Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Inner Mongolia, Hohhot 010051, China. Electronic address: dongchaoyi@imut.edu.cn.

PMID: 35151665
DOI: 10.1016/j.jneumeth.2022.109502

Abstract

Background: In the study of brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs), how to improve the classification accuracies of BCIs has always been the focus of researchers. Canonical correlation analysis (CCA) is widely used in BCI systems of SSVEPs because of its rapidity and scalability. However, the classical CCA algorithm always encounters the difficulty of low accuracy in a short time.

New method: For targetless stimuli, this paper proposes a fusion algorithm (CCA-CWT-SVM) that is combined with CCA, a continuous wavelet transform, and a support vector machine (SVM) to improve the low classification accuracies when a single feature extraction method is used.

Results: This fusion algorithm achieves high accuracies and information transfer rates (ITRs) in the SSVEP paradigm with few targets.

Comparison with existing methods and conclusions: Through the study of 400 groups of experimental data from 10 subjects, the results show that CCA-CWT-SVM has a classification accuracy of 91.76% within 2 s and an ITR of 48.92 bits/min, which are 10.88% and 13.18 bits/min higher than those of the standard CCA. Compared with a mainstream EEG decoding algorithm, filter bank canonical correlation analysis (FBCCA), the classification accuracy and ITR of the CCA-CWT-SVM algorithm also improved (4.45% and 5.69 bit/min, respectively). Using a dataset from Tsinghua University (THU), we also showed that the fusion algorithm is better than the classical algorithms. The CCA-CWT-SVM algorithm obtained an 89.1% accuracy and a 39.91 bit/min ITR in a time window of 2 s. The results were significantly improved compared with those of CCA and the FBCCA (CCA: 79.44% and 28.23 bits/min, FBCCA: 84.03% and 33.4 bits/min). Hence, this work provides an experimental basis for designing an SSVEP-based BCI system with a high task classification accuracy in some crucial biomedical applications.

Keywords: Canonical correlation analysis; Feature extraction; Steady-state visual evoked potential; Support vector machine; Wavelet coefficients.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Brain-Computer Interfaces*
Electroencephalography / methods
Evoked Potentials, Visual
Humans
Photic Stimulation
Support Vector Machine