Abstract:

To identify the psychological load of operators in hazardous operations and improve the reliability of man‐machine systems， the psychological load was induced by the detonation of energy?containing materials， and the heart rate， EEG （electroencephalogram）， and eye movement signals of 30 subjects were collected for psychological load identification under the resting state and psychological load. Firstly， the paired t‐test and rank sum test were used to statistically analyze the collected heart rate， EEG and eye movement signals. Eight EEG， three eye movement， and nine heart rate features were significantly changed under the resting state and psychological load. Secondly， Pearson correlation analysis， maximum relevance minimum redundancy （MRMR） algorithm and principal component analysis （PCA） were applied to reduce dimension of the physiological indexes obtained from the preliminary selection. Finally， the physiological indicators obtained after dimensionality reduction based on the above three methods were used for psychological load identification by Logistic Regression， KNN， SVM， XG‐Boost， Decision Tree， and Random Forest machine learning methods. The results showed that the Random Forest machine learning method has better identification performance （ACC=0.917， SN=1.0， SP=0.857， F1=0.909， AUC=0.971） based on MRMR’s psychological load feature selection results. The current research provides a theoretical basis for the effective identification of the psychological load of operators in hazardous operations.

Key words: hazardous operation, psychological load identification, physiological signal, machine learning