ReviewFrontiers in psychology2023
Deep learning-based EEG emotion recognition: Current trends and future perspectives.
Review in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Digital Horizons: Enhancing Autism Support with Augmented Reality.Journal of autism and developmental disorders · 2026Pooled it
- A Review of Deep Learning Techniques for EEG-Based Emotion Recognition: Models, Methods, and Datasets.F1000Research · 2025Pooled it
- Mapping research on ICT addiction: a comprehensive review of Internet, smartphone, social media, and gaming addictions.Frontiers in psychology · 2025Pooled it
- Cyborg-swarm cooperation and game via affective-based brain-machine interface.National science review · 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
- Explainable Temporal Deep Learning for EEG-Based Depression Detection Using Resting-State Brain Dynamics.International journal of methods in psychiatric research · 2026Article
- UDC-SNN: An Uncertainty-Aware Dynamic Cascading Framework with Spiking Neural Network for Balancing Performance and Energy in Multimodal Emotion Recognition.Sensors (Basel, Switzerland) · 2026Article
- [A group-level stimulus-aware self-supervised soft contrastive learning framework for electroencephalogram emotion recognition].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Development of a Measurement Procedure for Emotional States Detection Based on Single-Channel Ear-EEG: A Proof-of-Concept Study.Sensors (Basel, Switzerland) · 2026Article
- Emotion recognition based on the temporal patterns of electroencephalogram signals and electrodermal response signals using the TRANSFORMER network.Frontiers in neuroscience · 2026Article
- EED-CL: Extended EEG Deformer with Contrastive Learning for Robust Emotion Recognition.Bioengineering (Basel, Switzerland) · 2025Article
- EEG-Based Prediction of Stress Responses to Naturalistic Decision-Making Stimuli in Police Cadets.Sensors (Basel, Switzerland) · 2025Article
- Attention-Based Transfer Enhancement Network for Cross-Corpus EEG Emotion Recognition.Sensors (Basel, Switzerland) · 2025Article
- Segmentation-enhanced approach for emotion detection from EEG signals using the fuzzy C-mean and SVM.Scientific reports · 2025Article
- A Comprehensive Review of Multimodal Emotion Recognition: Techniques, Challenges, and Future Directions.Biomimetics (Basel, Switzerland) · 2025Review
- The Next Frontier in Brain Monitoring: A Comprehensive Look at In-Ear EEG Electrodes and Their Applications.Sensors (Basel, Switzerland) · 2025Review
- Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.Scientific reports · 2025Article
- An intelligent emotion prediction system using improved sand cat optimization technique based on EEG signals.Scientific reports · 2025Article
- Advancing Emotionally Aware Child-Robot Interaction with Biophysical Data and Insight-Driven Affective Computing.Sensors (Basel, Switzerland) · 2025Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic electroencephalogram (EEG) emotion recognition is a challenging component of human-computer interaction (HCI). Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have been employed increasingly to learn high-level feature representations for EEG emotion recognition. This paper aims to provide an up-to-date and comprehensive survey of EEG emotion recognition, especially for various deep learning techniques in this area. We provide the preliminaries and basic knowledge in the literature. We review EEG emotion recognition benchmark data sets briefly. We review deep learning techniques in details, including deep belief networks, convolutional neural networks, and recurrent neural networks. We describe the state-of-the-art applications of deep learning techniques for EEG emotion recognition in detail. We analyze the challenges and opportunities in this field and point out its future directions.
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Registered trials
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.