Augmenting the Size of EEG datasets Using Generative Adversarial Networks

Sherif M. Abdelfattah, Ghodai M. Abdelrahman, Min Wang

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

83 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781509060146
DOIs
Publication statusPublished - 10 Oct 2018
Externally publishedYes
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Conference

Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/TerritoryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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