Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion Recognition
In recent years, transfer learning methods have found widespread applications in emotion recognition, especially in domains with limited labeled data. The significant distribution disparity between source and target domains in transfer learning often leads to detrimental negative transfer phenomena....
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11016662/ |
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| author | Xin Deng Xinyi Hong LiJiao Ai Xingchen Li Chenhui Li |
| author_facet | Xin Deng Xinyi Hong LiJiao Ai Xingchen Li Chenhui Li |
| author_sort | Xin Deng |
| collection | DOAJ |
| description | In recent years, transfer learning methods have found widespread applications in emotion recognition, especially in domains with limited labeled data. The significant distribution disparity between source and target domains in transfer learning often leads to detrimental negative transfer phenomena. To mitigate this issue, the judicious selection of source domain data has proven to be an effective strategy. However, traditional training data selection methods typically require predefined thresholds, which are not easy to set, and the model is trained separately from the training data selection process. In an effort to address this gap, we introduce the Multi-Source Reinforced Selective Domain Adaptation (MSRSDA) model. The MSRSDA model comprises three components: a data augmentation module, a data selector based on the actor-critic framework, and a domain adaptation module. The data augmentation module generates new samples for the data selector to consider. The data selector, acting as an actor, performs actions to select high-quality source domain data for optimization of the domain adaptation model. Additionally, we propose an innovative domain adaptation model that provides rewards to update the data selector. To the best of our knowledge, our work represents an initial attempt to apply reinforcement learning to EEG-based data selection. Extensive experiments on both the public SEED dataset and our in-house dataset demonstrate that the MSRSDA model achieves competitive performance for emotion recognition tasks. |
| format | Article |
| id | doaj-art-33376bd6997f4baf92284df74b3deeaf |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-33376bd6997f4baf92284df74b3deeaf2025-08-20T03:26:05ZengIEEEIEEE Access2169-35362025-01-0113948229483310.1109/ACCESS.2025.357451511016662Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion RecognitionXin Deng0https://orcid.org/0000-0003-1257-694XXinyi Hong1https://orcid.org/0009-0008-8032-3446LiJiao Ai2Xingchen Li3https://orcid.org/0000-0002-3691-9516Chenhui Li4Chongqing Key Laboratory of Germplasm Innovation and Utilization of Native Plants, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaChongqing Key Laboratory of Germplasm Innovation and Utilization of Native Plants, Chongqing, ChinaSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaIn recent years, transfer learning methods have found widespread applications in emotion recognition, especially in domains with limited labeled data. The significant distribution disparity between source and target domains in transfer learning often leads to detrimental negative transfer phenomena. To mitigate this issue, the judicious selection of source domain data has proven to be an effective strategy. However, traditional training data selection methods typically require predefined thresholds, which are not easy to set, and the model is trained separately from the training data selection process. In an effort to address this gap, we introduce the Multi-Source Reinforced Selective Domain Adaptation (MSRSDA) model. The MSRSDA model comprises three components: a data augmentation module, a data selector based on the actor-critic framework, and a domain adaptation module. The data augmentation module generates new samples for the data selector to consider. The data selector, acting as an actor, performs actions to select high-quality source domain data for optimization of the domain adaptation model. Additionally, we propose an innovative domain adaptation model that provides rewards to update the data selector. To the best of our knowledge, our work represents an initial attempt to apply reinforcement learning to EEG-based data selection. Extensive experiments on both the public SEED dataset and our in-house dataset demonstrate that the MSRSDA model achieves competitive performance for emotion recognition tasks.https://ieeexplore.ieee.org/document/11016662/Transfer learningdomain adaptationelectroencephalogram (EEG)reinforcement learningemotion recognitionbrain-computer interface |
| spellingShingle | Xin Deng Xinyi Hong LiJiao Ai Xingchen Li Chenhui Li Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion Recognition IEEE Access Transfer learning domain adaptation electroencephalogram (EEG) reinforcement learning emotion recognition brain-computer interface |
| title | Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion Recognition |
| title_full | Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion Recognition |
| title_fullStr | Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion Recognition |
| title_full_unstemmed | Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion Recognition |
| title_short | Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion Recognition |
| title_sort | multi source reinforced selective domain adaptation for cross subject and cross session eeg based emotion recognition |
| topic | Transfer learning domain adaptation electroencephalogram (EEG) reinforcement learning emotion recognition brain-computer interface |
| url | https://ieeexplore.ieee.org/document/11016662/ |
| work_keys_str_mv | AT xindeng multisourcereinforcedselectivedomainadaptationforcrosssubjectandcrosssessioneegbasedemotionrecognition AT xinyihong multisourcereinforcedselectivedomainadaptationforcrosssubjectandcrosssessioneegbasedemotionrecognition AT lijiaoai multisourcereinforcedselectivedomainadaptationforcrosssubjectandcrosssessioneegbasedemotionrecognition AT xingchenli multisourcereinforcedselectivedomainadaptationforcrosssubjectandcrosssessioneegbasedemotionrecognition AT chenhuili multisourcereinforcedselectivedomainadaptationforcrosssubjectandcrosssessioneegbasedemotionrecognition |