EEG-Based Cross-Subject Mental Fatigue Recognition

Abstract

Mental fatigue is common at work places, and it can lead to decreased attention, vigilance and cognitive performance, which is dangerous in the situations such as driving, vessel maneuvering, etc. By directly measuring the neurophysiological activities happening in the brain, electroencephalography (EEG) signal can be used as a good indicator of mental fatigue. A classic EEG-based brain state recognition system requires labeled data from the user to calibrate the classifier each time before the use. For fatigue recognition, we argue that it is not practical to do so since the induction of fatigue state is usually long and weary. It is desired that the system can be calibrated using readily available fatigue data, and be applied to a new user with adequate recognition accuracy. In this paper, we explore performance of cross-subject fatigue recognition algorithms using the recently published EEG dataset labeled with two levels of fatigue. We evaluate three categories of classification method: classic classifier such as logistic regression, transfer learning-enabled classifier using transfer component analysis, and deep-learning based classifier such as EEGNet. Our results show that transfer learning-enabled classifier can outperform the other two for cross-subject fatigue recognition on a consistent basis. Specifically, transfer component analysis (TCA) improves the cross-subject recognition accuracy to 72.70 % that is higher than using just logistic regression (LR) by 9.08 % and EEGNet by 8.72 - 12.86 %.