Evaluation of mental fatigue based on multipsychophysiological parameters and kernel learning algorithms

Mental fatigue is an extremely sophisticated phenomenon, which is influenced by the environment, the state of health, vitality and the capability of recovery. A single parameter cannot fully describe it. In this paper, the effects of long time sustained low-workload visual display terminal (VDT) task on psychology are investigated by subjective self-reporting measures. Then power spectral indices of HRV, the P300 components based on visual oddball and wavelet packet parameters of EEG are combined to analyze the impacts of prolonged visual display terminal (VDT) activity on autonomic nervous system and central nervous system. Finally, wavelet packet parameters of EEG are extracted as the features of brain activity in different mental fatigue states. Kernel principal component analysis (KPCA) and support vector machine (SVM) are jointly applied to differentiate two states. The statistic results show that the level of both subjective sleepiness and fatigue increase significantly from pre-task to post-task, which indicate that the long time VDT task induces the mental fatigue to the subjects. The predominant activity of autonomic nervous system of subjects turns to the sympathetic activity from parasympathetic activity after the task. The P300 components and wavelet packet parameters of EEG are strongly related with mental fatigue. Moreover, the joint KPCA-SVM method is able to effectively reduce the dimensionality of the feature vectors, speed up the convergence in the training of SVM and achieve a high recognition accuracy (87%) of mental fatigue state. Multipsychophysiological measures and KPCA-SVM method could be a promising tool for the evaluation of mental fatigue.