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Data augmentation for enhancing EEG-based emotion recognition with deep generative models.
Open accessAbstract
Objective. The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep generative models, we propose three methods for augmenting EEG training data to enhance the performance of emotion recognition models. Approach. Our proposed methods are based on two deep generative models, variational autoencoder (VAE) and generative adversarial network (GAN), and two data augmentation ways, full and partial usage strategies. For the full usage strategy, all of the generated data are augmented to the training dataset without judging the quality of the generated data, while for the partial usage, only high-quality data are selected and appended to the training dataset. These three methods are called conditional Wasserstein GAN (cWGAN), selective VAE (sVAE), and selective WGAN (sWGAN). Main results. To evaluate the effectiveness of these proposed methods, we perform a systematic experimental study on two public EEG datasets for emotion recognition, namely, SEED and DEAP. We first generate realistic-like EEG training data in two forms: power spectral density and differential entropy. Then, we augment the original training datasets with a different number of generated realistic-like EEG data. Finally, we train support vector machines and deep neural networks with shortcut layers to build affective models using the original and augmented training datasets. The experimental results demonstrate that our proposed data augmentation methods based on generative models outperform the existing data augmentation approaches such as conditional VAE, Gaussian noise, and rotational data augmentation. We also observe that the number of generated data should be less than 10 times of the original training dataset to achieve the best performance. Significance. The augmented training datasets produced by our proposed sWGAN method significantly enhance the performance of EEG-based emotion recognition models.
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“Experiments show that two representations of EEG signals were suitable for the requirement of the artificial datasets that enhances the performance of the classifier.”
From Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review (2021) · cites this paper as Luo et al., 2020
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EEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508
Feng, L., Cheng, C., Zhao, M., Deng, H., & Zhang, Y. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3198688 · Cited by 165
GANSER: A Self-Supervised Data Augmentation Framework for EEG-Based Emotion Recognition
Zhang, Z., Liu, Y., & Zhong, S.-H. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3170369 · Cited by 154
Samal, P., & Hashmi, M. F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10690-2 · Cited by 170
Generative adversarial networks in EEG analysis: an overview
Habashi, A. G., Azab, A. M., Eldawlatly, S., & Aly, G. M. (2023) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-023-01169-w · Cited by 145
Metadata
- Authors
- Luo, Y., Zhu, L.-Z., Wan, Z.-Y., & Lu, B.-L.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Lu, Bao-Liang
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2020-10-01
- Volume
- 17
- Issue
- 5
- Pages
- 056021
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W3083255269
- Cited by
- 162
- References
- 51
- Citations by year
- 2026: 15 · 2025: 28 · 2024: 39 · 2023: 37 · 2022: 33 · 2021: 8 · 2020: 2
- FWCI
- 19.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61673266)
- 111 Project
- National Key Research and Development Program of China (2017YFB1002501)
- SJTU Trans-med Awards Research (WF540162605)
- Fundamental Research Funds for the Central Universities
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- EEG-based emotion recognition, data augmentation, deep generative models, variational autoencoder, generative adversarial networks, conditional Wasserstein GAN, data scarcity, power spectral density, differential entropy, affective model, SEED dataset, DEAP dataset
- MeSH terms
- Machine Learning, Algorithms, Electroencephalography, Emotions, Neural Networks, Computer
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