  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.
