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Cited by 122EEG and Brain-Computer InterfacesEmotion and Mood RecognitionImbalanced Data Classification Techniques

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Augmenting The Size of EEG datasets Using Generative Adversarial Networks

Abdelfattah, Sherif M., Abdelrahman, Ghodai M., Wang, Min·2018·2018 International Joint Conference on Neural Networks (IJCNN)
DOI 10.1109/ijcnn.2018.8489727
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Abstract

Electroencephalography (EEG) is one of the most promising methods in the field of Brain-Computer Interfaces (BCIs) due to its rich time-domain resolution and the availability of advanced and portable sensor technology. One of the major challenges for EEG signal analysis is the small size of its datasets as it is usually demanding for human subjects to perform lengthy experiments. Consequently, this challenge can limit the performance of EEG signal classification models. In this paper, we propose a novel generative adversarial network (GAN) model that can learn the statistical characteristics of the EEG signal and augment its datasets size to enhance the performance of classification models. Results show that the proposed model significantly outperforms other generative models on the utilized EEG dataset. Furthermore, it significantly enhances the performance of classification models working on small size EEG datasets after augmenting them with generated samples.

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References 26 · Cited by 122

Research works printed but not linked: 18

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Metadata

Authors
Abdelfattah, S. M., Abdelrahman, G. M., & Wang, M.
Affiliations
  • UNSW Sydney (AU)
  • Helwan University (EG)
ORCID
Venue
2018 International Joint Conference on Neural Networks (IJCNN)
Publisher
IEEE
Published
2018-07
Pages
1-6
ISSN
2161-4407 (ISSN-L)
DOI
10.1109/ijcnn.2018.8489727
OpenAlex
W2897035501
Cited by
122
References
26
Citations by year
2026: 2 · 2025: 12 · 2024: 17 · 2023: 29 · 2022: 21 · 2021: 20 · 2020: 15 · 2019: 6
FWCI
17.58
Type
conference paper
Language
en
Open access
closed
Locations
International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
Funders
Australian Government
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
Keywords
generative adversarial networks, EEG classification, brain-computer interface, data augmentation

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CoChat. (2026). Citation check: Augmenting The Size of EEG datasets Using Generative Adversarial Networks. https://app.cochat.ai/papers/10.1109/ijcnn.2018.8489727
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Abdelfattah, S. M., Abdelrahman, G. M., & Wang, M. (2018). Augmenting The Size of EEG datasets Using Generative Adversarial Networks. 2018 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2018.8489727