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.

中文翻译：

---

疲劳对稳态运动视觉诱发电位的影响：基于缩放运动的脑机接口的优化刺激参数。

背景与目的

在基于闪烁的稳态视觉诱发电位（SSVEP）脑机接口（BCI）中，由于长时间重复的视觉刺激，系统性能下降。为了减少由于视觉疲劳引起的性能下降，已经提出了基于缩放运动的稳态运动视觉诱发电位（SSMVEPs）范例。在这项研究中，对范式的刺激参数进行了优化，以缓解由于视觉疲劳而导致SSMVEP的检测精度下降。

方法

比较了八个具有不同刺激参数的基于缩放运动的SSMVEP范例。更改了图形的大小，亮度，颜色和形状，以及屏幕的刺激范围和刷新率的频率范围和间隔，以确定具有高识别精度和减少疲劳效果的最佳范例。还针对四个疲劳水平计算了SSMVEP的信噪比（SNR）。此外，在进行中的活动期间，脑电图（EEG）α和theta谱带的功率谱密度被计算用于刺激实验，以评估刺激任务开始和结束时的疲劳。

结果

所有刺激SSMVEP范例均显示出较高的准确性。亮度，颜色和形状的变化不会影响识别精度，也不会影响每个刺激块的较高刺激频率或较低频率间隔。但是，刺激较小的范式达到了最高的平均目标选择准确度，为97.2％，而标准范式为94.9％。此外，由于疲劳，它的识别精度几乎降低了零。从疲劳级别1到级别4，与标准范式相比，在进行刺激活动期间，较小的基于缩放运动的SSMVEP的SSMVEP的SNR降低幅度较小，而alpha /θ比的降低则较小。

结论

对于基于缩放运动的SSMVEP范例，更改多个刺激参数可以产生与标准范例相同的高性能。此外，使用较小的刺激可以减少由疲劳引起的精度降低，因为在诱发的SSMVEP状态下SNR的降低可忽略不计，并且在进行中的活动过程中α/θ指数的降低低于标准范式。