Emotion recognition based on convolutional gated recurrent units with attention
Studying brain activity and deciphering the information in electroencephalogram (EEG) signals has become an emerging research field, and substantial advances have been made in the EEG-based classification of emotions. However, using different EEG features and complementarity to discriminate other em...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2023.2289833 |
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| author | Zhu Ye Yuan Jing Qinghua Wang Pengrui Li Zhihong Liu Mingjing Yan Yongqing Zhang Dongrui Gao |
| author_facet | Zhu Ye Yuan Jing Qinghua Wang Pengrui Li Zhihong Liu Mingjing Yan Yongqing Zhang Dongrui Gao |
| author_sort | Zhu Ye |
| collection | DOAJ |
| description | Studying brain activity and deciphering the information in electroencephalogram (EEG) signals has become an emerging research field, and substantial advances have been made in the EEG-based classification of emotions. However, using different EEG features and complementarity to discriminate other emotions is still challenging. Most existing models extract a single temporal feature from the EEG signal while ignoring the crucial temporal dynamic information, which, to a certain extent, constrains the classification capability of the model. To address this issue, we propose an Attention-Based Depthwise Parameterized Convolutional Gated Recurrent Unit (AB-DPCGRU) model and validate it with the mixed experiment on the SEED and SEED-IV datasets. The experimental outcomes revealed that the accuracy of the model outperforms the existing state-of-the-art methods, which confirmed the superiority of our approach over currently popular emotion recognition models. |
| format | Article |
| id | doaj-art-34a31ee1aec2456ca38c172fdb7b79e9 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-34a31ee1aec2456ca38c172fdb7b79e92025-08-20T02:01:45ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.2289833Emotion recognition based on convolutional gated recurrent units with attentionZhu Ye0Yuan Jing1Qinghua Wang2Pengrui Li3Zhihong Liu4Mingjing Yan5Yongqing Zhang6Dongrui Gao7Chengdu University of Information Technology, Chengdu, People’s Republic of ChinaChengdu University of Information Technology, Chengdu, People’s Republic of ChinaHubi Wuhan Public Security Bureau, Wuhan City, People’s Republic of ChinaChengdu University of Information Technology, Chengdu, People’s Republic of ChinaChengdu University of Information Technology, Chengdu, People’s Republic of ChinaChengdu University of Information Technology, Chengdu, People’s Republic of ChinaChengdu University of Information Technology, Chengdu, People’s Republic of ChinaChengdu University of Information Technology, Chengdu, People’s Republic of ChinaStudying brain activity and deciphering the information in electroencephalogram (EEG) signals has become an emerging research field, and substantial advances have been made in the EEG-based classification of emotions. However, using different EEG features and complementarity to discriminate other emotions is still challenging. Most existing models extract a single temporal feature from the EEG signal while ignoring the crucial temporal dynamic information, which, to a certain extent, constrains the classification capability of the model. To address this issue, we propose an Attention-Based Depthwise Parameterized Convolutional Gated Recurrent Unit (AB-DPCGRU) model and validate it with the mixed experiment on the SEED and SEED-IV datasets. The experimental outcomes revealed that the accuracy of the model outperforms the existing state-of-the-art methods, which confirmed the superiority of our approach over currently popular emotion recognition models.https://www.tandfonline.com/doi/10.1080/09540091.2023.2289833EEGemotion recognitionattention mechanismdepthwise parameterised convolutionalgate recurrent neural unit |
| spellingShingle | Zhu Ye Yuan Jing Qinghua Wang Pengrui Li Zhihong Liu Mingjing Yan Yongqing Zhang Dongrui Gao Emotion recognition based on convolutional gated recurrent units with attention Connection Science EEG emotion recognition attention mechanism depthwise parameterised convolutional gate recurrent neural unit |
| title | Emotion recognition based on convolutional gated recurrent units with attention |
| title_full | Emotion recognition based on convolutional gated recurrent units with attention |
| title_fullStr | Emotion recognition based on convolutional gated recurrent units with attention |
| title_full_unstemmed | Emotion recognition based on convolutional gated recurrent units with attention |
| title_short | Emotion recognition based on convolutional gated recurrent units with attention |
| title_sort | emotion recognition based on convolutional gated recurrent units with attention |
| topic | EEG emotion recognition attention mechanism depthwise parameterised convolutional gate recurrent neural unit |
| url | https://www.tandfonline.com/doi/10.1080/09540091.2023.2289833 |
| work_keys_str_mv | AT zhuye emotionrecognitionbasedonconvolutionalgatedrecurrentunitswithattention AT yuanjing emotionrecognitionbasedonconvolutionalgatedrecurrentunitswithattention AT qinghuawang emotionrecognitionbasedonconvolutionalgatedrecurrentunitswithattention AT pengruili emotionrecognitionbasedonconvolutionalgatedrecurrentunitswithattention AT zhihongliu emotionrecognitionbasedonconvolutionalgatedrecurrentunitswithattention AT mingjingyan emotionrecognitionbasedonconvolutionalgatedrecurrentunitswithattention AT yongqingzhang emotionrecognitionbasedonconvolutionalgatedrecurrentunitswithattention AT dongruigao emotionrecognitionbasedonconvolutionalgatedrecurrentunitswithattention |