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EEGEpochNet: self-supervised contrastive learning for automated EEG epoch rejection with multi-level feature
Tengfei Gao1, Xiaoyu Hu1, Dan Chen2
1National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, People's Republic of China.
Journal of Neural Engineering
|February 23, 2026
Summary
EEGEpochNet accurately rejects bad electroencephalography (EEG) epochs using a novel deep learning approach. This automated method enhances data reliability and reduces manual inspection, outperforming existing techniques, especially with limited labeled data.
Area of Science:
- Neuroscience
- Machine Learning
- Biomedical Engineering
Background:
- Raw electroencephalography (EEG) data analysis necessitates effective rejection of artifact-corrupted epochs to ensure signal integrity.
- Current automated EEG artifact rejection methods face challenges in parameter optimization, adaptability to diverse scenarios, and dependency on extensive labeled datasets.
Purpose of the Study:
- To introduce EEGEpochNet, an end-to-end deep learning model designed for accurate and automated rejection of bad EEG epochs.
- To develop a robust framework that minimizes manual inspection and improves the reliability of EEG data analysis.
Main Methods:
- EEGEpochNet integrates a multi-branch 1D-CNN with U-Net for multi-level feature extraction, capturing scale-invariant patterns.
- Bidirectional GRUs model temporal electrophysiological dynamics for artifact differentiation.
- Self-supervised contrastive learning is employed to learn domain-invariant EEG representations from unlabeled data, reducing label dependency.
Main Results:
- EEGEpochNet achieved superior performance across semi-simulated and real-world pediatric and adult EEG datasets, with F1-scores of 93.05%, 95.33%, and 84.41%, respectively.
- The self-supervised learning component demonstrated significant advantages over supervised methods when labeled data were scarce.
Conclusions:
- EEGEpochNet offers a parameter-efficient and adaptable framework for automated EEG artifact rejection.
- The model facilitates reliable EEG analysis, paving the way for clinical-grade automation in neuroscience research and diagnostics.
