  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.
