ArticleScientific reports2024
TPRO-NET: an EEG-based emotion recognition method reflecting subtle changes in emotion.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Exercise-induced neuropeptidergic and neurochemical neuroadaptation in stress regulation and emotional disorders.Acta neurologica Belgica · 2026Review
- Proactive concussion prediction using symmetry-aware multiscale explainable hybrid deep learning and multimodal data fusion.Scientific reports · 2026Article
- [An electroencephalogram-based emotion recognition method using multi-branch convolutional neural networks and Transformer].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers.Bioengineering (Basel, Switzerland) · 2026Article
- EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network.Scientific reports · 2026Article
- A novel and accurate EEG emotion classification model based on multiple attention local binary patterns.BMC medicine · 2026Article
- TransTCNet: Transformer-Based Temporal-Contextual Network for Low-Latency Typing Interfaces on Edge Devices.Biomimetics (Basel, Switzerland) · 2026Article
- Article
- Subject-Independent Depression Recognition from EEG Using an Improved Bidirectional LSTM with Dynamic Vector Routing.Bioengineering (Basel, Switzerland) · 2026Article
- Hybrid fuzzy machine learning models optimized with meta-heuristics for accurate EEG-based neurological assessment.Scientific reports · 2026Article
- Video-dominant emotion recognition for portable EEG-based devices.Scientific reports · 2026Article
- A deep learning approach to emotionally intelligent AI for improved learning outcomes.Scientific reports · 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
- A multi-task EEG emotion recognition method based on emotion-dimension coupling constraints.Scientific reports · 2026Article
- Application of a parametric supermatrix multi-task multimodal deep learning model based on electroencephalogram and peripheral physiological signal fusion in emotion recognition.Frontiers in neuroscience · 2026Article
- Nightingale: A multimodal approach combining EEG signals and audio features to enhance music therapy.PloS one · 2026Article
- Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.Frontiers in computational neuroscience · 2026Article
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3 authors.
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Abstract
Emotion recognition based on Electroencephalogram (EEG) has been applied in various fields, including human-computer interaction and healthcare. However, for the popular Valence-Arousal-Dominance emotion model, researchers often classify the dimensions into high and low categories, which cannot reflect subtle changes in emotion. Furthermore, there are issues with the design of EEG features and the efficiency of transformer. To address these issues, we have designed TPRO-NET, a neural network that takes differential entropy and enhanced differential entropy features as input and outputs emotion categories through convolutional layers and improved transformer encoders. For our experiments, we categorized the emotions in the DEAP dataset into 8 classes and those in the DREAMER dataset into 5 classes. On the DEAP and the DREAMER datasets, TPRO-NET achieved average accuracy rates of 97.63%/97.47%/97.88% and 98.18%/98.37%/98.40%, respectively, on the Valence/Arousal/Dominance dimension for the subject-dependent experiments. Compared to other advanced methods, TPRO-NET demonstrates superior performance.
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