Despite significant recent progress in the area of Brain-Computer Interface  , 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 performance generalisation issues across differentsubjects 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 analyses demonstrate the efficacy of our generated data, leading to significant improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35\% with the use of  synthetic data.
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.
A
(BCI)
R
performance
predictive
R
differentsubjects
different subjects
A
,
R
analyses demonstrate
analysis demonstrates
D
significant
A
such