  Modern machine learning tools have shown promise in detecting symptoms of
neurological disorders. However, current approaches typically train a unique
classifier for each subject. This subject-specific training scheme requires
long labeled recordings from each patient, thus failing to detect symptoms in
new patients with limited recordings. This paper introduces an unsupervised
domain adaptation approach based on adversarial networks to enable few-shot,
cross-subject epileptic seizure detection. Using adversarial learning, features
from multiple patients were encoded into a subject-invariant space and a
discriminative model was trained on subject-invariant features to make
predictions. We evaluated this approach on the intracranial EEG (iEEG)
recordings from 9 patients with epilepsy. Our approach enabled cross-subject
seizure detection with a 9.4\% improvement in 1-shot classification accuracy
compared to the conventional subject-specific scheme.
