A comparative survey of SSVEP recognition algorithms based on template matching of training trials
International Journal of Intelligent Computing and Cybernetics
ISSN: 1756-378X
Article publication date: 7 April 2022
Issue publication date: 7 March 2023
Abstract
Purpose
Steady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to its characteristics of high accuracy and information transfer rate (ITR). To recognize the SSVEP components in collected EEG trials, a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years. In this paper, a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.
Design/methodology/approach
To survey and compare the recently proposed recognition algorithms for SSVEP, this paper regarded the conventional canonical correlated analysis (CCA) as the baseline, and selected individual template CCA (ITCCA), multi-set CCA (MsetCCA), task related component analysis (TRCA), latent common source extraction (LCSE) and a sum of squared correlation (SSCOR) for comparison.
Findings
For the horizontal comparative of the six surveyed recognition algorithms, this paper adopted the “Tsinghua JFPM-SSVEP” data set and compared the average recognition performance on such data set. The comparative contents including: recognition accuracy, ITR, correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation. Based on the optimal time duration of stimulus presentation, the author has also compared the efficiency of the six compared algorithms. To measure the influence of different parameters, the number of training trials, the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.
Originality/value
Based on the comparative results, this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes, real-time and computational complexity. Finally, the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.
Keywords
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. 62106049). No potential conflict of interest was reported by the author. The author thanks the anonymous reviewers for their comments to improve the quality of the paper. The author also thanks Tsinghua BCI lab for their share of JFPM-SSVEP data set, and the data set can be downloaded at: Benchmark data set: http://bci.med.tsinghua.edu.cn/.
Citation
Luo, T.-J. (2023), "A comparative survey of SSVEP recognition algorithms based on template matching of training trials", International Journal of Intelligent Computing and Cybernetics, Vol. 16 No. 1, pp. 46-67. https://doi.org/10.1108/IJICC-01-2022-0002
Publisher
:Emerald Publishing Limited
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