mlsanityData to 7 Oct 2026

Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography

arXiv 2207.01391 ↗First posted 4 Jul 2022cs.LG eess.SPcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 4 Jul 2022.

Builds on

11 of 29 references.

References · 11

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  3. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 181
  4. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  5. 2018Anomaly Detection using One-Class Neural Networkscited by 82
  6. 2019GODS: Generalized One-class Discriminative Subspaces for Anomaly Detectioncited by 64
  7. 2021Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Imagescited by 45
  8. 2020Machine-Learning-Based Diagnostics of EEG Pathologycited by 32
  9. 2020Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoderscited by 29
  10. 2021Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizurescited by 7
  11. 2018A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Modelscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 5; 2026: 2.

11 papers.

  1. 2024Self-supervised Learning for Electroencephalogram: A Systematic Surveycited by 19
  2. 2023Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detectioncited by 10
  3. 2025A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challengescited by 4
  4. 2025The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary studycited by 4
  5. 2025Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnosticscited by 3
  6. 2024Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learningcited by 1
  7. 2025NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detectioncited by 1
  8. 2023TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration Networkcited by 0
  9. 2025FADE: Forecasting for Anomaly Detection on ECGcited by 0
  10. 2026Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosiscited by 0
  11. 2026ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterizationcited by 0