Augmenting The Size of EEG datasets Using Generative Adversarial Networks

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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