Xiaoke Chai1, Zhimin Zhang1, Kai Guan1, Tengyu Zhang2, Jinxiu Xu3, Haijun Niu4. 1. Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China. 2. National Research Center for Rehabilitation Technical Aids, Beijing 100176, China. 3. Shanxi Deayea Tong'an Technology Co., Ltd, Shanxi 030000, China. 4. Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China. Electronic address: hjniu@buaa.edu.cn.
Abstract
BACKGROUND AND OBJECTIVE: In flicker-based steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI), the system performance decreases due to prolonged repeated visual stimulation. To reduce the performance decrease due to visual fatigue, the zoom motion based steady-state motion visual evoked potentials (SSMVEPs) paradigm had been proposed. In this study, the stimulation parameters of the paradigm are optimised to mitigate the decrease in detection accuracy for SSMVEP due to visual fatigue. METHODS: Eight zoom motion-based SSMVEP paradigms with different stimulation parameters were compared. The graph size, luminance, colour, and shape, as well as the frequency range and interval of the stimulation and refresh rate of the screen was changed to determine the optimal paradigm with high recognition accuracy and reduced fatigue effects. The signal-to-noise ratio (SNR) of SSMVEP was also calculated for four fatigue levels. Moreover, the power spectral density of electroencephalograph (EEG) alpha and theta bands during ongoing activity was calculated for the stimulation experiment to evaluate fatigue at the start and end of the stimulation task. RESULTS: All stimulation SSMVEP paradigms exhibited high accuracies. Changes in luminance, colour, and shape did not impact the recognition accuracy, nor did a higher stimulation frequency or lower frequency interval of each stimulation block. However, the paradigm with smaller stimulus achieved the highest average target selection accuracy of 97.2%, compared to 94.9% for the standard paradigm. Furthermore, it exhibited almost zero reduction in recognition accuracy due to fatigue. From fatigue level 1 to level 4, the smaller zoom motion-based SSMVEP exhibited a lower decrease in the SNR of SSMVEP and a lower alpha/theta ratio decrease during ongoing stimulation activity compared to the standard paradigm. CONCLUSIONS: For a zoom motion-based SSMVEP paradigm, changing multiple stimulation parameters can lead to the same high performance as the standard paradigm. Moreover, using a smaller stimulus can reduce the accuracy decrease caused by fatigue because the SNR decrease in the evoked SSMVEP state was negligible and the alpha/theta index decrease during ongoing activity was lower than that for the standard paradigm.
BACKGROUND AND OBJECTIVE: In flicker-based steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI), the system performance decreases due to prolonged repeated visual stimulation. To reduce the performance decrease due to visual fatigue, the zoom motion based steady-state motion visual evoked potentials (SSMVEPs) paradigm had been proposed. In this study, the stimulation parameters of the paradigm are optimised to mitigate the decrease in detection accuracy for SSMVEP due to visual fatigue. METHODS: Eight zoom motion-based SSMVEP paradigms with different stimulation parameters were compared. The graph size, luminance, colour, and shape, as well as the frequency range and interval of the stimulation and refresh rate of the screen was changed to determine the optimal paradigm with high recognition accuracy and reduced fatigue effects. The signal-to-noise ratio (SNR) of SSMVEP was also calculated for four fatigue levels. Moreover, the power spectral density of electroencephalograph (EEG) alpha and theta bands during ongoing activity was calculated for the stimulation experiment to evaluate fatigue at the start and end of the stimulation task. RESULTS: All stimulation SSMVEP paradigms exhibited high accuracies. Changes in luminance, colour, and shape did not impact the recognition accuracy, nor did a higher stimulation frequency or lower frequency interval of each stimulation block. However, the paradigm with smaller stimulus achieved the highest average target selection accuracy of 97.2%, compared to 94.9% for the standard paradigm. Furthermore, it exhibited almost zero reduction in recognition accuracy due to fatigue. From fatigue level 1 to level 4, the smaller zoom motion-based SSMVEP exhibited a lower decrease in the SNR of SSMVEP and a lower alpha/theta ratio decrease during ongoing stimulation activity compared to the standard paradigm. CONCLUSIONS: For a zoom motion-based SSMVEP paradigm, changing multiple stimulation parameters can lead to the same high performance as the standard paradigm. Moreover, using a smaller stimulus can reduce the accuracy decrease caused by fatigue because the SNR decrease in the evoked SSMVEP state was negligible and the alpha/theta index decrease during ongoing activity was lower than that for the standard paradigm.