ArticleCognitive neurodynamics2020
EEG-based emotion recognition using 4D convolutional recurrent neural network.
Article in Cognitive neurodynamics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network.Computational and mathematical methods in medicine · 2021Pooled it
- GLSTNet: A Global-Local Spatial Relations and Temporal Dynamics Network for EEG-Based Emotion Recognition.Biosensors · 2026Article
- EEG and ECG Wearable Biosensor-Based Affective State Analysis Using Deep Learning over 6G IoT Healthcare Networks.Biosensors · 2026Article
- Self-Learning Multimodal Emotion Recognition Based on Multi-Scale Dilated Attention.Brain sciences · 2026Article
- EEG-Based Emotion Estimation Model Integrating Structural and Time-Series Information Based on Deep Learning Architecture Optimization.Sensors (Basel, Switzerland) · 2026Article
- Discrimination of temporal fatigue-related states and inferred brain-function coupling in subway drivers under emotional influence.Frontiers in human neuroscience · 2026Article
- A dual-branch network with brain region-constrained attention for EEG emotion recognition.Frontiers in neuroscience · 2026Article
- Objective Emotion Assessment Using a Triple Attention Network for an EEG-Based Brain-Computer Interface.Brain sciences · 2025Article
- MSDSANet: Multimodal Emotion Recognition Based on Multi-Stream Network and Dual-Scale Attention Network Feature Representation.Sensors (Basel, Switzerland) · 2025Article
- Exploring physical and functional EEG connectivity with multilayer graph transformer convolutional networks for emotion recognition.Frontiers in human neuroscience · 2025Article
- CNN-BiLSTM and DC-IGN fusion model and piecewise exponential attenuation optimization: an innovative approach to improve EEG emotion recognition performance.Frontiers in computational neuroscience · 2025Article
- Set-pMAE: spatial-spEctral-temporal based parallel masked autoEncoder for EEG emotion recognition.Cognitive neurodynamics · 2024Article
- Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.Sensors (Basel, Switzerland) · 2024Review
- SST-CRAM: spatial-spectral-temporal based convolutional recurrent neural network with lightweight attention mechanism for EEG emotion recognition.Cognitive neurodynamics · 2024Article
- CSA-SA-CRTNN: A Dual-Stream Adaptive Convolutional Cyclic Hybrid Network Combining Attention Mechanisms for EEG Emotion Recognition.Brain sciences · 2024Article
- Channel attention convolutional aggregation network based on video-level features for EEG emotion recognition.Cognitive neurodynamics · 2024Article
- EEG decoding for effects of visual joint attention training on ASD patients with interpretable and lightweight convolutional neural network.Cognitive neurodynamics · 2024Article
- Review
- EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution.Sensors (Basel, Switzerland) · 2024Article
- Research on Fatigue Driving Detection Technology Based on CA-ACGAN.Brain sciences · 2024Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
In this paper, we present a novel method, called four-dimensional convolutional recurrent neural network, which integrating frequency, spatial and temporal information of multichannel EEG signals explicitly to improve EEG-based emotion recognition accuracy. First, to maintain these three kinds of information of EEG, we transform the differential entropy features from different channels into 4D structures to train the deep model. Then, we introduce CRNN model, which is combined by convolutional neural network (CNN) and recurrent neural network with long short term memory (LSTM) cell. CNN is used to learn frequency and spatial information from each temporal slice of 4D inputs, and LSTM is used to extract temporal dependence from CNN outputs. The output of the last node of LSTM performs classification. Our model achieves state-of-the-art performance both on SEED and DEAP datasets under intra-subject splitting. The experimental results demonstrate the effectiveness of integrating frequency, spatial and temporal information of EEG for emotion recognition.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.