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 in...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,realtime and computational complexity.Finally,the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.展开更多
基金supported by National Natural Science Foundation of China(Grant No.62106049).
文摘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,realtime and computational complexity.Finally,the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.
