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Among various types of brain computer interfaces (BCIs), steady state visually evoked potential (SSVEP) based BCIs can provide high information transfer rate (ITR), however the users could suffer serious fatigue that may induce discomfort, health hazards and deterioration of system performance. To overcome the fatigue obstacle, the first step is to detect the fatigue accurately, reliably and quickly. This paper proposes an approach based on the wavelet entropy of the measured EEG to fatigue detection in real time when using an SSVEP-BCI. Specifically, the wavelet analysis is first applied to the EEG, resulting in the approximation and detail components at different levels. The sample entropy values of these components are then calculated to generate features for classification. Experimental results identified the entropy of the lower frequency components (0 &#x2013; 4.6875Hz) as the most important feature. The proposed wavelet entropy improved the fatigue detection accuracy to 87.7&#x0025; from 65.1&#x0025; by the traditional entropy method, when distinguishing subjects&#x2019; mental states between alert (before task) and fatigue (after task). Furthermore, the detection accuracy based on the state of art multiple conventional fatigue indices can be improved from 91.9&#x0025; to 96.5&#x0025; by replacing the delta band amplitude with the new wavelet entropy feature.

Fatigue Detection in SSVEP-BCIs Based on Wavelet Entropy of EEG