  Electroencephalogram (EEG) signals are effective tools towards seizure
analysis where one of the most important challenges is accurate detection of
seizure events and brain regions in which seizure happens or initiates.
However, all existing machine learning-based algorithms for seizure analysis
require access to the labeled seizure data while acquiring labeled data is very
labor intensive, expensive, as well as clinicians dependent given the
subjective nature of the visual qualitative interpretation of EEG signals. In
this paper, we propose to detect seizure channels and clips in a
self-supervised manner where no access to the seizure data is needed. The
proposed method considers local structural and contextual information embedded
in EEG graphs by employing positive and negative sub-graphs. We train our
method through minimizing contrastive and generative losses. The employ of
local EEG sub-graphs makes the algorithm an appropriate choice when accessing
to the all EEG channels is impossible due to complications such as skull
fractures. We conduct an extensive set of experiments on the largest seizure
dataset and demonstrate that our proposed framework outperforms the
state-of-the-art methods in the EEG-based seizure study. The proposed method is
the only study that requires no access to the seizure data in its training
phase, yet establishes a new state-of-the-art to the field, and outperforms all
related supervised methods.
