Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis. (arXiv:2208.07448v1 [cs.LG])

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 …

analysis application arxiv detection graphs learning lg self-supervised learning supervised learning