Cited by 162Emotion and Mood RecognitionEEG and Brain-Computer InterfacesBiometric Identification and Security

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Data augmentation for enhancing EEG-based emotion recognition with deep generative models.

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Luo, Yun, Zhu, Li-Zhen, Wan, Zi-Yu, Lu, Bao-Liang·2020·Journal of Neural Engineering

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Abstract

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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Authors: Luo, Y., Zhu, L.-Z., Wan, Z.-Y., & Lu, B.-L.
Affiliations: Shanghai Jiao Tong University (CN)
Corresponding author: Lu, Bao-Liang
ORCID: Luo, Yun
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
DOI: 10.1088/1741-2552/abb580
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: publishingsupport.iopscience.iop.org/iop-standard/v1 (vor, 11 d)
iopscience.iop.org/info/page/text-and-data-mining (tdm, 11 d)
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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Data augmentation for enhancing EEG-based emotion recognition with deep generative models.

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