ArticleCognitive neurodynamics2024
EEG-based emotion recognition using a temporal-difference minimizing neural network.
Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- 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
- Depletion Effects Reduce the Preference for Distraction (but Not Reappraisal) During Emotion Regulation.PsyCh journal · 2025Article
- EmoTrans attention based emotion recognition using EEG signals and facial analysis with expert validation.Scientific reports · 2025Article
- Transformer-Driven Affective State Recognition from Wearable Physiological Data in Everyday Contexts.Sensors (Basel, Switzerland) · 2025Article
- CSA-SA-CRTNN: A Dual-Stream Adaptive Convolutional Cyclic Hybrid Network Combining Attention Mechanisms for EEG Emotion Recognition.Brain sciences · 2024Article
- Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain-Computer Interface.Brain sciences · 2024Article
- Exploring Brain Dynamics via EEG and Steady-State Activation Map Networks in Music Composition.Brain sciences · 2024Article
- HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.Frontiers in neuroscience · 2024Article
- A novel signal channel attention network for multi-modal emotion recognition.Frontiers in neurorobotics · 2024Article
- Multi-source domain adaptation for EEG emotion recognition based on inter-domain sample hybridization.Frontiers in human neuroscience · 2024Article
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4 authors.
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Abstract
Electroencephalogram (EEG) emotion recognition plays an important role in human-computer interaction. An increasing number of algorithms for emotion recognition have been proposed recently. However, it is still challenging to make efficient use of emotional activity knowledge. In this paper, based on prior knowledge that emotion varies slowly across time, we propose a temporal-difference minimizing neural network (TDMNN) for EEG emotion recognition. We use maximum mean discrepancy (MMD) technology to evaluate the difference in EEG features across time and minimize the difference by a multibranch convolutional recurrent network. State-of-the-art performances are achieved using the proposed method on the SEED, SEED-IV, DEAP and DREAMER datasets, demonstrating the effectiveness of including prior knowledge in EEG emotion recognition.
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