Residual-Enhanced VAE-GAN for EEG Data Augmentation in Motor Imagery Classification
Motor imagery is a widely studied paradigm in Brain-Computer Interfaces (BCIs) systems, where electroencephalography (EEG) signals are used to decode brain activity. However, subject-specific variability leads to significant differences in EEG distributions across individuals, making it challenging...
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| Veröffentlicht in: | IEEE International Conference on Consumer Electronics-China (Online) S. 603 - 604 |
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16.07.2025
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| ISSN: | 2575-8284 |
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| Abstract | Motor imagery is a widely studied paradigm in Brain-Computer Interfaces (BCIs) systems, where electroencephalography (EEG) signals are used to decode brain activity. However, subject-specific variability leads to significant differences in EEG distributions across individuals, making it challenging for models trained on existing subject data to generalize well to unseen individuals. To improve the diversity of training data and enhance model generalization, this paper proposes an EEG data augmentation method, termed Residual-Enhanced Variational Autoencoder-Generative Adversarial Network (REVG). The framework combines a Variational Autoencoder for EEG reconstruction with a Wasserstein Generative Adversarial Network to generate realistic residual signals, enriching EEG data distribution. Experimental results on the BCI Competition IV 2b dataset demonstrate that REVG achieves an average classification accuracy of 76.83%, outperforming the baseline and other data augmentation methods. |
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| AbstractList | Motor imagery is a widely studied paradigm in Brain-Computer Interfaces (BCIs) systems, where electroencephalography (EEG) signals are used to decode brain activity. However, subject-specific variability leads to significant differences in EEG distributions across individuals, making it challenging for models trained on existing subject data to generalize well to unseen individuals. To improve the diversity of training data and enhance model generalization, this paper proposes an EEG data augmentation method, termed Residual-Enhanced Variational Autoencoder-Generative Adversarial Network (REVG). The framework combines a Variational Autoencoder for EEG reconstruction with a Wasserstein Generative Adversarial Network to generate realistic residual signals, enriching EEG data distribution. Experimental results on the BCI Competition IV 2b dataset demonstrate that REVG achieves an average classification accuracy of 76.83%, outperforming the baseline and other data augmentation methods. |
| Author | Tang, Haiyuan Zhao, Wenshan |
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| Snippet | Motor imagery is a widely studied paradigm in Brain-Computer Interfaces (BCIs) systems, where electroencephalography (EEG) signals are used to decode brain... |
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| SubjectTerms | Accuracy Autoencoders Brain modeling Brain-computer interfaces Data augmentation Data models Domain Generalization Electroencephalography Image reconstruction Motor Imagery Motors Residual Modeling Training data |
| Title | Residual-Enhanced VAE-GAN for EEG Data Augmentation in Motor Imagery Classification |
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