Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography
This work addresses the challenge of detecting anomalies in EEG for clinicians, offering a method that can monitor previously unseen statuses, though it appears incremental by building on existing anomaly detection strategies.
The paper tackles the problem of anomaly detection in EEG by proposing a task-oriented self-supervised learning approach that uses normal EEGs and expert knowledge about abnormal data to train a feature extractor, resulting in improved feature representations as demonstrated on three EEG datasets.
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.