ArticleEntropy (Basel, Switzerland)2025
A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition.
Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- BoviFusionNet: A Lightweight Edge-Deployable AI System for Cattle Behavior Recognition in Livestock Monitoring.Veterinary sciences · 2026Article
- Multi-Entropy Feature Concatenation for Data-Efficient Cross-Subject Classification of Alzheimer's Disease and Frontotemporal Dementia from Single-Channel EEG.Entropy (Basel, Switzerland) · 2026Article
- Analysis of the generalization ability of graph neural networks in cross-subject EEG emotion recognition.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Emotion recognition based on feature weight analysis of multiple physiological signals.PloS one · 2026Article
- Assessing Complexity in Physiological Systems Through Biomedical Signals Analysis II.Entropy (Basel, Switzerland) · 2025Article
- A Novel Multi-Scale Entropy Approach for EEG-Based Lie Detection with Channel Selection.Entropy (Basel, Switzerland) · 2025Article
- A Novel Co-Designed Multi-Domain Entropy and Its Dynamic Synapse Classification Approach for EEG Seizure Detection.Entropy (Basel, Switzerland) · 2025Article
- An innovative brain-inspired neuronal competition model for electroencephalography emotion recognition.Digital healthArticle
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10 authors.
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Abstract
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.
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