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Simulating brain signals : creating synthetic EEG data via neural-based generative models for improved SSVEP classification.

Aznan, N.K.N. and Atapour-Abarghouei, A. and Bonner, S. and Connolly, J.D. and Al Moubayed, N. and Breckon, T.P. (2019) 'Simulating brain signals : creating synthetic EEG data via neural-based generative models for improved SSVEP classification.', in 2019 International Joint Conference on Neural Networks (IJCNN) ; proceedings. , pp. 1-8.


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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:07 March 2019
Date deposited:10 May 2019
Date of first online publication:2019
Date first made open access:13 November 2019

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