A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition
Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human–computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by...
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2025-01-01
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| author | Jiawen Li Guanyuan Feng Chen Ling Ximing Ren Xin Liu Shuang Zhang Leijun Wang Yanmei Chen Xianxian Zeng Rongjun Chen |
| author_facet | Jiawen Li Guanyuan Feng Chen Ling Ximing Ren Xin Liu Shuang Zhang Leijun Wang Yanmei Chen Xianxian Zeng Rongjun Chen |
| author_sort | Jiawen Li |
| collection | DOAJ |
| description | Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human–computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.e., delta, theta, alpha, beta, and gamma, from EEG signals, followed by the acquisition of multi-entropy features, including Spectral Entropy (PSDE), Singular Spectrum Entropy (SSE), Sample Entropy (SE), Fuzzy Entropy (FE), Approximation Entropy (AE), and Permutation Entropy (PE). Then, such entropies are fused into a matrix to represent complex and dynamic characteristics of EEG, denoted as the Brain Rhythm Entropy Matrix (BREM). Next, Dynamic Time Warping (DTW), Mutual Information (MI), the Spearman Correlation Coefficient (SCC), and the Jaccard Similarity Coefficient (JSC) are applied to measure the similarity between the unknown testing BREM data and positive/negative emotional samples for classification. Experiments were conducted using the DEAP dataset, aiming to find a suitable scheme regarding similarity measures, time windows, and input numbers of channel data. The results reveal that DTW yields the best performance in similarity measures with a 5 s window. In addition, the single-channel input mode outperforms the single-region mode. The proposed method achieves 84.62% and 82.48% accuracy in arousal and valence classification tasks, respectively, indicating its effectiveness in reducing data dimensionality and computational complexity while maintaining an accuracy of over 80%. Such performances are remarkable when considering limited data resources as a concern, which opens possibilities for an innovative entropy fusion method that can help to design portable EEG-based emotion-aware devices for daily usage. |
| format | Article |
| id | doaj-art-b928adfde2de40f28b7f3b78de406df1 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-b928adfde2de40f28b7f3b78de406df12025-01-24T13:31:59ZengMDPI AGEntropy1099-43002025-01-012719610.3390/e27010096A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion RecognitionJiawen Li0Guanyuan Feng1Chen Ling2Ximing Ren3Xin Liu4Shuang Zhang5Leijun Wang6Yanmei Chen7Xianxian Zeng8Rongjun Chen9School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaDepartment of Electrical and Computer Engineering, University of Macau, Macau 999078, ChinaSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaEmotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human–computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.e., delta, theta, alpha, beta, and gamma, from EEG signals, followed by the acquisition of multi-entropy features, including Spectral Entropy (PSDE), Singular Spectrum Entropy (SSE), Sample Entropy (SE), Fuzzy Entropy (FE), Approximation Entropy (AE), and Permutation Entropy (PE). Then, such entropies are fused into a matrix to represent complex and dynamic characteristics of EEG, denoted as the Brain Rhythm Entropy Matrix (BREM). Next, Dynamic Time Warping (DTW), Mutual Information (MI), the Spearman Correlation Coefficient (SCC), and the Jaccard Similarity Coefficient (JSC) are applied to measure the similarity between the unknown testing BREM data and positive/negative emotional samples for classification. Experiments were conducted using the DEAP dataset, aiming to find a suitable scheme regarding similarity measures, time windows, and input numbers of channel data. The results reveal that DTW yields the best performance in similarity measures with a 5 s window. In addition, the single-channel input mode outperforms the single-region mode. The proposed method achieves 84.62% and 82.48% accuracy in arousal and valence classification tasks, respectively, indicating its effectiveness in reducing data dimensionality and computational complexity while maintaining an accuracy of over 80%. Such performances are remarkable when considering limited data resources as a concern, which opens possibilities for an innovative entropy fusion method that can help to design portable EEG-based emotion-aware devices for daily usage.https://www.mdpi.com/1099-4300/27/1/96electroencephalography (EEG)multi-entropy fusionbrain rhythmssingle-channelemotion recognition |
| spellingShingle | Jiawen Li Guanyuan Feng Chen Ling Ximing Ren Xin Liu Shuang Zhang Leijun Wang Yanmei Chen Xianxian Zeng Rongjun Chen A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition Entropy electroencephalography (EEG) multi-entropy fusion brain rhythms single-channel emotion recognition |
| title | A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition |
| title_full | A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition |
| title_fullStr | A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition |
| title_full_unstemmed | A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition |
| title_short | A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition |
| title_sort | resource efficient multi entropy fusion method and its application for eeg based emotion recognition |
| topic | electroencephalography (EEG) multi-entropy fusion brain rhythms single-channel emotion recognition |
| url | https://www.mdpi.com/1099-4300/27/1/96 |
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