Mental fatigue is one of the major factors leading to human errors. To avoid failures caused by mental fatigue, researchers are working on ways to detect/monitor fatigue using different types of signals. Electroencephalography (EEG) signal is one of the most popular methods to recognize mental fatigue since it directly measures the neurophysiological activities in the brain. Current EEG-based fatigue recognition algorithms are usually subject-specific, which means a classifier needs to be trained per subject. However, as fatigue may need a relatively long period to induce, collecting training data from each new user could be time-consuming and troublesome. Calibration-free methods are desired but also challenging since significant variability of physiological signals exists among different subjects. In this paper, we proposed algorithms using inter-subject transfer learning for EEG-based mental fatigue recognition, which did not need a calibration. To explore the influence of the number of EEG channels on the algorithms’ accuracy, we also compared the cases of using one channel only and multiple channels. Random forest was applied to choose the channel that has the most distinguishable features. A public EEG fatigue dataset recorded during driving was used to validate the algorithms. EEG data from 11 subjects were selected from the dataset and leave-one-subject-out cross-validation was employed. The channel from the occipital lobe is selected when only one channel is desired. The proposed transfer learning-based algorithms using Maximum Independence Domain Adaptation (MIDA) achieved an accuracy of 73.01% with all thirty channels, and using Transfer Component Analysis (TCA) achieved 68.00% with the one selected channel.

精神疲劳是导致人为错误的主要因素之一。为了避免精神疲劳引起的故障，研究人员正在研究使用不同类型信号检测/监视疲劳的方法。脑电图（EEG）信号是识别精神疲劳的最流行方法之一，因为它直接测量大脑中的神经生理活动。当前基于EEG的疲劳识别算法通常是特定于学科的，这意味着需要针对每个学科训练分类器。但是，由于疲劳可能需要较长的时间才能诱发，因此从每个新用户那里收集训练数据可能既耗时又麻烦。由于不同受试者之间存在生理信号的显着差异，因此希望采用无标定方法，但也具有挑战性。在本文中，我们提出了基于科目间迁移学习的基于EEG的精神疲劳识别算法，该算法无需校准。为了探讨脑电图通道数对算法准确性的影响，我们还比较了仅使用一个通道和多个通道的情况。应用随机森林来选择具有最明显特征的渠道。在驾驶过程中记录的公共EEG疲劳数据集用于验证算法。从数据集中选择11名受试者的EEG数据，并采用留一法则交叉验证。当只需要一个通道时，选择来自枕叶的通道。提议的使用最大独立域自适应（MIDA）的基于转移学习的算法在所有三十个通道中均达到了73.01％的精度，