Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification

Aznan, N.K.N.; Atapour-Abarghouei, A.; Bonner, S.; Connolly, J.D.; Al Moubayed, N.; Breckon, T.P.

Abstract

Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments. These include, but are not limited to, subject and session data variance, long and arduous calibration processes and predictive generalisation issues across different subjects or sessions. This implies that many downstream applications, including Steady State Visual Evoked Potential (SSVEP) based classification systems, can suffer from a shortage of reliable data. Generating meaningful and realistic synthetic data can therefore be of significant value in circumventing this problem. We explore the use of modern neural-based generative models trained on a limited quantity of EEG data collected from different subjects to generate supplementary synthetic EEG signal vectors, subsequently utilised to train an SSVEP classifier. Extensive experimental analysis demonstrates the efficacy of our generated data, leading to improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35% with the use of such synthetic data.

Citation

Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2019, December). Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification. Presented at International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary

• Presentation Conference Type: Conference Paper (published)
• Conference Name: International Joint Conference on Neural Networks (IJCNN)
• Acceptance Date: Mar 7, 2019
• Publication Date: Jul 14, 2019
• Deposit Date: Mar 25, 2019
• Publicly Available Date: Nov 13, 2019
• Pages: 1-8
• Series ISSN: 2161-4407
• Book Title: 2019 International Joint Conference on Neural Networks (IJCNN) ; proceedings
• DOI: https://doi.org/10.1109/ijcnn.2019.8852227
• Public URL: https://durham-repository.worktribe.com/output/1144574
• Related Public URLs: arXiv:1901.07429