  Systems that can automatically analyze EEG signals can aid neurologists by
reducing heavy workload and delays. However, such systems need to be first
trained using a labeled dataset. While large corpuses of EEG data exist, a
fraction of them are labeled. Hand-labeling data increases workload for the
very neurologists we try to aid. This paper proposes a semi-supervised learning
workflow that can not only extract meaningful information from large unlabeled
EEG datasets but also make predictions with minimal supervision, using labeled
datasets as small as 5 examples.
