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.

本文提出了基于对比学习的无监督自适应方法来建模具有噪声标签和少数受试者的生物信号，通过特定于个体的对比损失和对抗性训练来提高模型性能，以及使用时间序列数据增强技术来完善对比损失，实现了对脑电解码和心电异常检测任务的良好分类结果。