  Mental fatigue increases the risk of operator error in language comprehension
tasks. In order to prevent operator performance degradation, we used EEG
signals to assess the mental fatigue of operators in human-computer systems.
This study presents an experimental design for fatigue detection in language
comprehension tasks. We obtained EEG signals from a 14-channel wireless EEG
detector in 15 healthy participants. Each participant was given a cognitive
test of a language comprehension task, in the form of multiple choice
questions, in which pronoun references were selected between nominal and
surrogate sentences. In this paper, the 2400 EEG fragments collected are
divided into three data sets according to different utilization rates, namely
1200s data set with 50% utilization rate, 1500s data set with 62.5% utilization
rate, and 1800s data set with 75% utilization rate. In the aspect of feature
extraction, different EEG features were extracted, including time domain
features, frequency domain features and entropy features, and the effects of
different features and feature combinations on classification accuracy were
explored. In terms of classification, we introduced the Convolutional Neural
Network (CNN) method as the preferred method, It was compared with Least
Squares Support Vector Machines(LSSVM),Support Vector Machines(SVM),Logistic
Regression (LR), Random Forest(RF), Naive Bayes (NB), K-Nearest Neighbor (KNN)
and Decision Tree(DT).According to the results, the classification accuracy of
convolutional neural network (CNN) is higher than that of other classification
methods. The classification results show that the classification accuracy of
1200S dataset is higher than the other two datasets. The combination of
Frequency and entropy feature and CNN has the highest classification accuracy,
which is 85.34%.
