基于SSVEP-BCI的可穿戴疲劳检测系统

Wearable fatigue detection system for SSVEP-BCI

由于稳态视觉诱发电位的脑机接口(SSVEP-BCI)具有高准确性、高传输速率且无需训练,用户需要花费大量精力专注于视觉刺激以产生足够强的SSVEP,其中高亮度、频繁的低频刺激和单一任务十分容易使用户产生疲劳.针对用户疲劳问题,提出一种针对SSVEP-BCI的实时疲劳检测系统,该系统包括一个可穿戴式脑电设备的硬件设计和实现以及基于支持向量机的分类算法.基于该系统,对用于疲劳检测准确性的熵进行研究,并发现模糊熵与近似熵在检测中具有一致性,在疲劳变化微弱的情况下模糊熵变化更突出,而在疲劳变化明显的情况下近似熵的变化更显著.此外,对前额和枕叶信号进行比较,发现前额信号的分类准确性通常高于枕叶信号,同时复合准确性高于任何一种单独使用时的准确性.

Steady-state visual evoked potential-based brain-computer interfaces(SSVEP-BCI)are very popular for assistive control applications because of high accuracy,high transmission rate and no training required.However,users need to spend a lot of energy focusing on visual stimuli to generate strong enough SSVEP.Users are very fatigued due to high luminance,frequent low-frequency stimuli and single task.In this thesis,a real-time fatigue detection system for the SSVEP-BCI is proposed.The system completes the hardware design and implementation of a wearable electroencephalogram(EEG)device,and the classification algorithm based on support vector machines(SVM).On this basis,this project investigates the entropy used for fatigue detection accuracy and finds that fuzzy entropy and approximate entropy are consistent in detection.The fuzzy entropy change is more prominent in the case of weak fatigue changes while the approximate entropy change is more significant in the case of significant fatigue changes.In addition,this paper compares prefrontal and occipital lobe signals and finds that prefrontal signals usually have higher classification accuracy than occipital lobe signals.And the composite accuracy is higher than the accuracy of either one when used alone.