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

Lookup NU author(s): Dr Nik Khadijah Nik Aznan, Dr Amir Atapour Abarghouei, Stephen Bonner, Dr Jason Connolly, Dr Noura Al Moubayed

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2019.

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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.


Publication metadata

Author(s): Nik Aznan NK, Atapour-Abarghouei A, Bonner S, Connolly JD, Al Moubayed N, Breckon TP

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Joint Conference on Neural Networks (IJCNN 2019)

Year of Conference: 2019

Online publication date: 30/09/2019

Acceptance date: 07/03/2019

Date deposited: 06/02/2021

ISSN: 2161-4407

Publisher: IEEE

URL: https://doi.org/10.1109/IJCNN.2019.8852227

DOI: 10.1109/IJCNN.2019.8852227

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728119854


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