Jing-Ran Su Jing-Ran Su, Qiu-Sheng Li Jing-Ran Su, Qian-Li Zhang Qiu-Sheng Li, Jun-Yong Hu Qian-Li Zhang
{"title":"EEG Emotion Recognition Method Based on 3D Feature Map and Improved DenseNet","authors":"Jing-Ran Su Jing-Ran Su, Qiu-Sheng Li Jing-Ran Su, Qian-Li Zhang Qiu-Sheng Li, Jun-Yong Hu Qian-Li Zhang","doi":"10.53106/199115992023063403008","DOIUrl":null,"url":null,"abstract":"\n Emotion, as a high-level function of the human brain, has a great impact on people’s mental health. To fully con-sider EEG signals’ spatial information and time-frequency information, and realize human-computer interaction better. This paper proposes an improved DenseNet emotion recognition model based on 3D feature map. By extracting the differential entropy features of the θ, α, β and γ frequency bands of the EEG signals, and combining the position mapping relationship of the EEG channel electrodes, a three-dimensional feature map is constructed, and then the improved densely connected convolutional network (DenseNet) is used for secondary feature extraction and classification. To verify the effectiveness of this method, a classification experiment including positive, neutral and negative emotions is carried out on the SEED data set. The classification accuracy rates obtained in the single-subject experiment and the all-subject experiment are 98.51% and 98.68%, respectively. The experimental results show that the method of 3D feature map combined with feature reuse can get high-precision classification results, which provides a new direction for emotion recognition.\n \n","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"92 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"電腦學刊","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53106/199115992023063403008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Emotion, as a high-level function of the human brain, has a great impact on people’s mental health. To fully con-sider EEG signals’ spatial information and time-frequency information, and realize human-computer interaction better. This paper proposes an improved DenseNet emotion recognition model based on 3D feature map. By extracting the differential entropy features of the θ, α, β and γ frequency bands of the EEG signals, and combining the position mapping relationship of the EEG channel electrodes, a three-dimensional feature map is constructed, and then the improved densely connected convolutional network (DenseNet) is used for secondary feature extraction and classification. To verify the effectiveness of this method, a classification experiment including positive, neutral and negative emotions is carried out on the SEED data set. The classification accuracy rates obtained in the single-subject experiment and the all-subject experiment are 98.51% and 98.68%, respectively. The experimental results show that the method of 3D feature map combined with feature reuse can get high-precision classification results, which provides a new direction for emotion recognition.