ArticleCognitive neurodynamics2022
4D attention-based neural network for EEG emotion recognition.
Article in Cognitive neurodynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Emotion recognition using spectral-spatial attention multi-temporal scale network: EEG study.Brain informatics · 2026Article
- Self-Learning Multimodal Emotion Recognition Based on Multi-Scale Dilated Attention.Brain sciences · 2026Article
- An automated extraction of spectral-temporal and spatial-temporal features of EEG for emotion detection.Brain informatics · 2025Article
- A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects.Brain informatics · 2024Article
- Set-pMAE: spatial-spEctral-temporal based parallel masked autoEncoder for EEG emotion recognition.Cognitive neurodynamics · 2024Article
- SST-CRAM: spatial-spectral-temporal based convolutional recurrent neural network with lightweight attention mechanism for EEG emotion recognition.Cognitive neurodynamics · 2024Article
- CATM: A Multi-Feature-Based Cross-Scale Attentional Convolutional EEG Emotion Recognition Model.Sensors (Basel, Switzerland) · 2024Article
- EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution.Sensors (Basel, Switzerland) · 2024Article
- Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.Scientific reports · 2024Article
- Research on Fatigue Driving Detection Technology Based on CA-ACGAN.Brain sciences · 2024Article
- EEG-based emotion recognition using a temporal-difference minimizing neural network.Cognitive neurodynamics · 2024Article
- Emotion Classification Based on Transformer and CNN for EEG Spatial-Temporal Feature Learning.Brain sciences · 2024Article
- CIT-EmotionNet: convolution interactive transformer network for EEG emotion recognition.PeerJ. Computer science · 2024Article
- A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications.PeerJ. Computer science · 2024Article
- Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.Frontiers in neuroscience · 2024Article
- Directional Spatial and Spectral Attention Network (DSSA Net) for EEG-based emotion recognition.Frontiers in neurorobotics · 2024Article
- HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.Frontiers in neuroscience · 2024Article
- STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition.Frontiers in neuroscience · 2024Article
- Feature hypergraph representation learning on spatial-temporal correlations for EEG emotion recognition.Cognitive neurodynamics · 2023Article
- EEG Emotion Recognition by Fusion of Multi-Scale Features.Brain sciences · 2023Article
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6 authors.
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Abstract
Electroencephalograph (EEG) emotion recognition is a significant task in the brain-computer interface field. Although many deep learning methods are proposed recently, it is still challenging to make full use of the information contained in different domains of EEG signals. In this paper, we present a novel method, called four-dimensional attention-based neural network (4D-aNN) for EEG emotion recognition. First, raw EEG signals are transformed into 4D spatial-spectral-temporal representations. Then, the proposed 4D-aNN adopts spectral and spatial attention mechanisms to adaptively assign the weights of different brain regions and frequency bands, and a convolutional neural network (CNN) is utilized to deal with the spectral and spatial information of the 4D representations. Moreover, a temporal attention mechanism is integrated into a bidirectional Long Short-Term Memory (LSTM) to explore temporal dependencies of the 4D representations. Our model achieves state-of-the-art performances on both DEAP, SEED and SEED-IV datasets under intra-subject splitting. The experimental results have shown the effectiveness of the attention mechanisms in different domains for EEG emotion recognition.
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