SynthesisComputational and mathematical methods in medicine2021
Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network.
Synthesis in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Adaptive frequency band attention-guided CNN-BiLSTM for Spatial-Spectral-Temporal EEG emotion recognition.Brain informatics · 2026Article
- A survey on data augmentation for EEG-based emotion recognition and cognitive workload decoding.Frontiers in neuroscience · 2026Review
- An automated extraction of spectral-temporal and spatial-temporal features of EEG for emotion detection.Brain informatics · 2025Article
- Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.Sensors (Basel, Switzerland) · 2025Review
- Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases.Scientific reports · 2025Article
- Music-evoked emotions classification using vision transformer in EEG signals.Frontiers in psychology · 2024Article
- MISNet: multi-source information-shared EEG emotion recognition network with two-stream structure.Frontiers in neuroscience · 2024Article
- STSNet: a novel spatio-temporal-spectral network for subject-independent EEG-based emotion recognition.Health information science and systems · 2023Article
- Generative adversarial networks in EEG analysis: an overview.Journal of neuroengineering and rehabilitation · 2023Review
- Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder.Frontiers in neuroscience · 2023Article
- Identification of emotions evoked by music via spatial-temporal transformer in multi-channel EEG signals.Frontiers in neuroscience · 2023Article
- Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network.Computational intelligence and neuroscience · 2022Article
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2 authors.
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Abstract
Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely used in the field of EEG emotion recognition and has achieved remarkable results. However, due to the cost of data collection, most EEG datasets have only a small amount of EEG data, and the sample categories are unbalanced in these datasets. These problems will make it difficult for the deep learning model to predict the emotional state. In this paper, we propose a new sample generation method using generative adversarial networks to solve the problem of EEG sample shortage and sample category imbalance. In experiments, we explore the performance of emotion recognition with the frequency band correlation and frequency band separation computational models before and after data augmentation on standard EEG-based emotion datasets. Our experimental results show that the method of generative adversarial networks for data augmentation can effectively improve the performance of emotion recognition based on the deep learning model. And we find that the frequency band correlation deep learning model is more conducive to emotion recognition.
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