Gongen Han

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Quan Wang

Xi'an Institute of Optics and Precision Mechanics

Shaokang Yin

Xi'an Institute of Optics and Precision Mechanics

Xi Liu

Shandong University

Xiaojie Wei

Chinese Academy of Sciences

Feiyu Zhu

Carnegie Mellon University

Runqi Zhang

Shenzhen University

Bingliang Hu

Xi'an Institute of Optics and Precision Mechanics

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Xi'an Institute of Optics and Precision Mechanics

Chinese Academy of Sciences

Spectral Imaging Laboratory (United States)

University of Chinese Academy of Sciences

Ruijin Hospital

Southwest Medical University

Peking University

Yale University

China Electric Power Research Institute

Xuzhou Medical College

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Intelligent Classification for Emotional Issues by Deep Learning Network on EEG Signal Processing

2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) (2022)

Shaokang Yin Feiyu Zhu Xiaojie Wei Gongen Han Runqi Zhang

Identifying the risk of emotional issues is of great significance to the development of adolescents. Electroencephalography (EEG) signals can reflect brain activities, and imply the emotional status, therefore can be used to identify emotional issues. In this study, we designed an experimental paradigm for high school adolescents by displaying emotion-inducing pictures as stimuli and recorded their EEG signals simultaneously. The EEG signals was preprocessed and analyzed. In this paper we applied a convolutional network EEGNet to classify four emotional issues in adolescents: depression symptom, manic symptom, anxiety symptom and the control group. The results showed that the classification accuracy of the four groups can reach 94.24%. In addition, this paper explored using different types of picture stimuli on the classification and reached a result above chance level. This work extended the previous work on the classification of emotional issues to four categories, and achieved a good classification accuracy.

SIGNAL (programming language)

10.1109/eebda53927.2022.9744774

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