ArticleFrontiers in computational neuroscience2021
Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder.
Article in Frontiers in computational neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Self-Learning Multimodal Emotion Recognition Based on Multi-Scale Dilated Attention.Brain sciences · 2026Article
- Graph-theoretical analysis of EEG-based functional connectivity during emotional experience in virtual reality for emotion recognition.Scientific reports · 2025Article
- Emotion Classification Based on Transformer and CNN for EEG Spatial-Temporal Feature Learning.Brain sciences · 2024Article
- FCAN-XGBoost: A Novel Hybrid Model for EEG Emotion Recognition.Sensors (Basel, Switzerland) · 2023Article
- Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition.Sensors (Basel, Switzerland) · 2022Article
- The multiscale 3D convolutional network for emotion recognition based on electroencephalogram.Frontiers in neuroscience · 2022Article
- Exploration of effective electroencephalography features for the recognition of different valence emotions.Frontiers in neuroscience · 2022Article
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6 authors.
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Abstract
As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.
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