Abstract
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 – 4.6875Hz) as the most important feature. The proposed wavelet entropy improved the fatigue detection accuracy to 87.7% from 65.1% by the traditional entropy method, when distinguishing subjects’ 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% to 96.5% by replacing the delta band amplitude with the new wavelet entropy feature.
Highlights
Brain-computer interfaces (BCIs) provide alternative communication methodologies between human brain and external devices
This paper proposes an approach based on the wavelet entropy of the measured EEG to fatigue detection in real time when using an state visually evoked potential (SSVEP)-brain computer interfaces (BCIs)
The steady state visually evoked potential (SSVEP) based BCIs have advantages such as relatively high signal to noise ratio (SNR), high information transfer rate and low training requirement [1,2,3,4] compared with other types of non-invasive BCIs
Summary
Brain-computer interfaces (BCIs) provide alternative communication methodologies between human brain and external devices. The steady state visually evoked potential (SSVEP) based BCIs have advantages such as relatively high signal to noise ratio (SNR), high information transfer rate and low training requirement [1,2,3,4] compared with other types of non-invasive BCIs. there are still several disadvantages of the SSVEP-BCIs that need improvements, such as users’ fatigue. Some techniques have been applied to reduce users’ fatigue in the SSVEP-BCIs, such as the use of high frequency or high duty cycle visual stimuli. For a better balance between fatigue alleviation and improving system performance, it is important to detect/evaluate users’ fatigue. Objective fatigue detection/evaluation via physiological signals such as EEG is the most popular choice for the scenarios. In practice, the EEG signals are usually non-stationary due to the changes of the subjects’ physiological states, meaning
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