ArticleFrontiers in neuroscience2022
The multiscale 3D convolutional network for emotion recognition based on electroencephalogram.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Balancing Local and Global Dependencies in EEG Emotion Recognition via Multi-Scale Hybrid Attention.Sensors (Basel, Switzerland) · 2026Article
- Emotion recognition based on feature weight analysis of multiple physiological signals.PloS one · 2026Article
- HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.Frontiers in neuroscience · 2024Article
- Spatial-Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection.Interdisciplinary sciences, computational life sciences · 2023Article
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5 authors.
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Abstract
Emotion recognition based on EEG (electroencephalogram) has become a research hotspot in the field of brain-computer interfaces (BCI). Compared with traditional machine learning, the convolutional neural network model has substantial advantages in automatic feature extraction in EEG-based emotion recognition. Motivated by the studies that multiple smaller scale kernels could increase non-linear expression than a larger scale, we propose a 3D convolutional neural network model with multiscale convolutional kernels to recognize emotional states based on EEG signals. We select more suitable time window data to carry out the emotion recognition of four classes (low valence vs. low arousal, low valence vs. high arousal, high valence vs. low arousal, and high valence vs. high arousal). The results using EEG signals in the DEAP and SEED-IV datasets show accuracies for our proposed emotion recognition network model (ERN) of 95.67 and 89.55%, respectively. The experimental results demonstrate that the proposed approach is potentially useful for enhancing emotional experience in BCI.
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