Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration
Since Electroencephalogram (EEG) is resistant to camouflage, it has been a reliable data source for objective emotion recognition. EEG is naturally multi-rhythm and multi-channel, based on which we can extract multiple features for further processing. In EEG-based emotion recognition, it is importan...
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10003248/ |
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| author | Yong Peng Honggang Liu Junhua Li Jun Huang Bao-Liang Lu Wanzeng Kong |
| author_facet | Yong Peng Honggang Liu Junhua Li Jun Huang Bao-Liang Lu Wanzeng Kong |
| author_sort | Yong Peng |
| collection | DOAJ |
| description | Since Electroencephalogram (EEG) is resistant to camouflage, it has been a reliable data source for objective emotion recognition. EEG is naturally multi-rhythm and multi-channel, based on which we can extract multiple features for further processing. In EEG-based emotion recognition, it is important to investigate whether there exist some common features shared by different emotional states, and the specific features associated with each emotional state. However, such fundamental problem is ignored by most of the existing studies. To this end, we propose a Joint label-Common and label-Specific Features Exploration (JCSFE) model for semi-supervised cross-session EEG emotion recognition in this paper. To be specific, JCSFE imposes the <inline-formula> <tex-math notation="LaTeX">$\ell _{\text {2,1}}$ </tex-math></inline-formula>-norm on the projection matrix to explore the label-common EEG features and simultaneously the <inline-formula> <tex-math notation="LaTeX">$\ell _{{1}}$ </tex-math></inline-formula>-norm is used to explore the label-specific EEG features. Besides, a graph regularization term is introduced to enforce the data local invariance property, i.e., similar EEG samples are encouraged to have the same emotional state. Results obtained from the SEED-IV and SEED-V emotional data sets experimentally demonstrate that JCSFE not only achieves superior emotion recognition performance in comparison with the state-of-the-art models but also provides us with a quantitative method to identify the label-common and label-specific EEG features in emotion recognition. |
| format | Article |
| id | doaj-art-d7ebcbfd507743dfb9d7085313e837cb |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
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| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-d7ebcbfd507743dfb9d7085313e837cb2025-08-20T03:07:37ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102023-01-013175976810.1109/TNSRE.2022.323310910003248Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features ExplorationYong Peng0https://orcid.org/0000-0003-1208-972XHonggang Liu1Junhua Li2Jun Huang3https://orcid.org/0000-0002-2022-5747Bao-Liang Lu4https://orcid.org/0000-0001-8359-0058Wanzeng Kong5https://orcid.org/0000-0002-0113-6968School of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaHDU-IMTO Joint Institute, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.School of Computer Science and Technology, Anhui University of Technology, Ma’anshan, ChinaDepartment of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, ChinaSchool of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaSince Electroencephalogram (EEG) is resistant to camouflage, it has been a reliable data source for objective emotion recognition. EEG is naturally multi-rhythm and multi-channel, based on which we can extract multiple features for further processing. In EEG-based emotion recognition, it is important to investigate whether there exist some common features shared by different emotional states, and the specific features associated with each emotional state. However, such fundamental problem is ignored by most of the existing studies. To this end, we propose a Joint label-Common and label-Specific Features Exploration (JCSFE) model for semi-supervised cross-session EEG emotion recognition in this paper. To be specific, JCSFE imposes the <inline-formula> <tex-math notation="LaTeX">$\ell _{\text {2,1}}$ </tex-math></inline-formula>-norm on the projection matrix to explore the label-common EEG features and simultaneously the <inline-formula> <tex-math notation="LaTeX">$\ell _{{1}}$ </tex-math></inline-formula>-norm is used to explore the label-specific EEG features. Besides, a graph regularization term is introduced to enforce the data local invariance property, i.e., similar EEG samples are encouraged to have the same emotional state. Results obtained from the SEED-IV and SEED-V emotional data sets experimentally demonstrate that JCSFE not only achieves superior emotion recognition performance in comparison with the state-of-the-art models but also provides us with a quantitative method to identify the label-common and label-specific EEG features in emotion recognition.https://ieeexplore.ieee.org/document/10003248/EEG emotion recognitiongraph regularizationlabel-common featureslabel-specific featuressemi-supervised regression |
| spellingShingle | Yong Peng Honggang Liu Junhua Li Jun Huang Bao-Liang Lu Wanzeng Kong Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration IEEE Transactions on Neural Systems and Rehabilitation Engineering EEG emotion recognition graph regularization label-common features label-specific features semi-supervised regression |
| title | Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration |
| title_full | Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration |
| title_fullStr | Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration |
| title_full_unstemmed | Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration |
| title_short | Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration |
| title_sort | cross session emotion recognition by joint label common and label specific eeg features exploration |
| topic | EEG emotion recognition graph regularization label-common features label-specific features semi-supervised regression |
| url | https://ieeexplore.ieee.org/document/10003248/ |
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