ArticleFrontiers in psychiatry2021
Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network.
Article in Frontiers in psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- A Comparative Analysis of EWT and EMD Techniques in the Diagnosis of Major Depressive Disorder Using EEG: Asymmetry Features and Explainability via SHAP.Diagnostics (Basel, Switzerland) · 2026Article
- Emotion recognition using spectral-spatial attention multi-temporal scale network: EEG study.Brain informatics · 2026Article
- Distinguishing Early Depression from Negative Emotion via Multi-Domain EEG Feature Fusion and Multi-Head Additive Attention Network.Entropy (Basel, Switzerland) · 2026Article
- MCEEGNet: a multi-cue EEG network for quantitative assessment of depression using emotional stimuli-induced EEG signals.Frontiers in artificial intelligence · 2026Article
- Closed-loop EEG-based neurofeedback and brain-computer interface interventions for mental health: a review.Frontiers in neuroscience · 2026Review
- Improving EEG based brain computer interface emotion detection with EKO ALSTM model.Scientific reports · 2025Article
- Emotional recognition technologies applied to health: review and challenges.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Review
- MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition.Brain sciences · 2024Article
- A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications.PeerJ. Computer science · 2024Article
- A novel methodology for emotion recognition through 62-lead EEG signals: multilevel heterogeneous recurrence analysis.Frontiers in physiology · 2024Article
- Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning.Brain sciences · 2023Article
- EEG-based high-performance depression state recognition.Frontiers in neuroscience · 2023Article
- Local domain generalization with low-rank constraint for EEG-based emotion recognition.Frontiers in neuroscience · 2023Article
- An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method.Frontiers in neuroscience · 2023Article
- GCFFrontiers in neuroscience · 2023Article
- Cross-Subject Emotion Recognition Using Fused Entropy Features of EEG.Entropy (Basel, Switzerland) · 2022Article
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6 authors.
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Abstract
The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-channel EEG signals. First, we use filtering and Euclidean alignment for data preprocessing. In the feature extraction, we use short-time Fourier transform and Hilbert-Huang transform to extract time-frequency features, and convolutional neural networks to extract spatial features. Finally, bi-directional long short-term memory explored the timing relationship. Before performing the convolution operation, according to the unique topology of the EEG channel, the EEG features are converted into 3D tensors. This study has achieved good results on two emotion databases: SEED and Emotional BCI of 2020 WORLD ROBOT COMPETITION. We applied this method to the recognition of depression based on EEG and achieved a recognition rate of more than 70% under the five-fold cross-validation. In addition, the subject-independent protocol on SEED data has achieved a state-of-the-art recognition rate, which exceeds the existing research methods. We propose a novel EEG emotion recognition framework for depression detection, which provides a robust algorithm for real-time clinical depression detection based on EEG.
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