Papers › Subject-Aware Contrastive Learning for Biosignals

Subject-Aware Contrastive Learning for Biosignals

30 Jun 2020arXiv:2007.04871archive 2025-07-28

Joseph Y. Cheng, Hanlin Goh, Kaan Dogrusoz, Oncel Tuzel, Erdrin Azemi

Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100). To handle these challenges, we propose a self-supervised approach based on contrastive learning to model biosignals with a reduced reliance on labeled data and with fewer subjects. In this regime of limited labels and subjects, intersubject variability negatively impacts model performance. Thus, we introduce subject-aware learning through (1) a subject-specific contrastive loss, and (2) an adversarial training to promote subject-invariance during the self-supervised learning. We also develop a number of time-series data augmentation techniques to be used with the contrastive loss for biosignals. Our method is evaluated on publicly available datasets of two different biosignals with different tasks: EEG decoding and ECG anomaly detection. The embeddings learned using self-supervision yield competitive classification results compared to entirely supervised methods. We show that subject-invariance improves representation quality for these tasks, and observe that subject-specific loss increases performance when fine-tuning with supervised labels.

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Code

zacharycbrown/ssl_baselines_for_biosignal_feature_extraction mentioned on GitHubpytorch report

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Tasks

Anomaly Detection Contrastive Learning Data Augmentation EEG EEG 4 classes EEG Left/Right hand Eeg Decoding Electroencephalogram (EEG)Person Identification Self-Supervised Learning Time Series Time Series Analysis

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Person Identification	EEG Motor Movement/Imagery Dataset	SSL	Accuracy	0.886	#1 of 3	Archive leaderboard	report
Person Identification	EEG Motor Movement/Imagery Dataset	Subject-invariant SSL Embedding & Linear Classifier	Accuracy	0.73	#2 of 3	Archive leaderboard	report
Person Identification	EEG Motor Movement/Imagery Dataset	Subject-specific SSL	Accuracy	0.684	#3 of 3	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1D CNN

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