  Recently, substantial progress has been made in the area of Brain-Computer
Interface (BCI) using modern machine learning techniques to decode and
interpret brain signals. While Electroencephalography (EEG) has provided a
non-invasive method of interfacing with a human brain, the acquired data is
often heavily subject and session dependent. This makes seamless incorporation
of such data into real-world applications intractable as the subject and
session data variance can lead to long and tedious calibration requirements and
cross-subject generalisation issues. Focusing on a Steady State Visual Evoked
Potential (SSVEP) classification systems, we propose a novel means of
generating highly-realistic synthetic EEG data invariant to any subject,
session or other environmental conditions. Our approach, entitled the Subject
Invariant SSVEP Generative Adversarial Network (SIS-GAN), produces synthetic
EEG data from multiple SSVEP classes using a single network. Additionally, by
taking advantage of a fixed-weight pre-trained subject classification network,
we ensure that our generative model remains agnostic to subject-specific
features and thus produces subject-invariant data that can be applied to new
previously unseen subjects. Our extensive experimental evaluation demonstrates
the efficacy of our synthetic data, leading to superior performance, with
improvements of up to 16 percentage points in zero-calibration classification
tasks when trained using our subject-invariant synthetic EEG signals.
