  Accurate automated analysis of electroencephalography (EEG) would largely
help clinicians effectively monitor and diagnose patients with various brain
diseases. Compared to supervised learning with labelled disease EEG data which
can train a model to analyze specific diseases but would fail to monitor
previously unseen statuses, anomaly detection based on only normal EEGs can
detect any potential anomaly in new EEGs. Different from existing anomaly
detection strategies which do not consider any property of unavailable abnormal
data during model development, a task-oriented self-supervised learning
approach is proposed here which makes use of available normal EEGs and expert
knowledge about abnormal EEGs to train a more effective feature extractor for
the subsequent development of anomaly detector. In addition, a specific two
branch convolutional neural network with larger kernels is designed as the
feature extractor such that it can more easily extract both larger scale and
small-scale features which often appear in unavailable abnormal EEGs. The
effectively designed and trained feature extractor has shown to be able to
extract better feature representations from EEGs for development of anomaly
detector based on normal data and future anomaly detection for new EEGs, as
demonstrated on three EEG datasets. The code is available at
https://github.com/ironing/EEG-AD.
