Fatigue Detection Using the Strength of Dominant Eeg Source: A Beamforming Approach

It is evident that the electroencephalogram (EEG) rhythms are slightly changed when the efficacy of mental activity declines (brain fatigue). Nonetheless, this slight change is not easily detectable by the so far suggested scalp EEG features. The goal of this paper is to propose an EEG-based biomarker, which has a congruity to the mental fatigue variation to detect the transition from non-fatigue to the fatigue mental state. The strength of the dominant EEG source, extracted by minimum variance beamformer (MVB), is proposed here as a discriminative feature to remarkably classify the two mental states. To assess the proposed scheme, EEG signals of 17 volunteers were recorded via 32 electrodes before and after taking an exhausting mental exam (3[Formula: see text]h) and the extracted EEG features were labeled as non-fatigue and fatigue, respectively. After removing the eye-blink effect, the proposed feature along with the conventional EEG features were extracted from the recorded EEGs and then applied to support vector machine (SVM) and 1-nearest neighbor (1NN) classifiers in order to differentiate these two mental states. The best result is achieved by applying the proposed feature to the SVM classifier providing 97.06% classification accuracy which is significantly ([Formula: see text]) superior to its counter parts.