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Mental fatigue increases the risk of operator error in language comprehensiontasks. In order to prevent operator performance degradation, we used EEGsignals to assess the mental fatigue of operators in human-computer systems.This study presents an experimental design for fatigue detection in languagecomprehension tasks. We obtained EEG signals from a 14-channel wireless EEGdetector in 15 healthy participants. Each participant was given a cognitivetest of a language comprehension task, in the form of multiple choicequestions, in which pronoun references were selected between nominal andsurrogate sentences. In this paper, the 2400 EEG fragments collected aredivided into three data sets according to different utilization rates, namely1200s data set with 50% utilization rate, 1500s data set with 62.5% utilizationrate, and 1800s data set with 75% utilization rate. In the aspect of featureextraction, different EEG features were extracted, including time domainfeatures, frequency domain features and entropy features, and the effects ofdifferent features and feature combinations on classification accuracy wereexplored. In terms of classification, we introduced the Convolutional NeuralNetwork (CNN) method as the preferred method, It was compared with LeastSquares Support Vector Machines(LSSVM),Support Vector Machines(SVM),LogisticRegression (LR), Random Forest(RF), Naive Bayes (NB), K-Nearest Neighbor (KNN)and Decision Tree(DT).According to the results, the classification accuracy ofconvolutional neural network (CNN) is higher than that of other classificationmethods. The classification results show that the classification accuracy of1200S dataset is higher than the other two datasets. The combination ofFrequency and entropy feature and CNN has the highest classification accuracy,which is 85.34%.

Identification of mental fatigue in language comprehension tasks based on EEG and deep learning