Cross-subject generalizable representation learning with class-subject dual labels for biosignals

Biosignals provide information about the onset of certain diseases or health conditions, aiding in diagnosing diseases and monitoring health conditions quickly and accurately. However, the inter-subject variability within biosignals hampers the model performance. In this study, we propose an inter-subject similar loss to learn representations robust to inter-subject variability. This loss promotes subject invariance, improves the generalizability of the representation, and allows better representations to be learned even with fewer training subjects. The proposed framework consists of two complementary loss functions: (1) supervised contrastive loss and (2) inter-subject similar loss. We evaluated the classification performance of the proposed method on three public biosignal datasets. The experimental results demonstrate that the proposed method outperforms the comparison methods and that the subject-invariant representation performs well on unseen subjects. Code is available at: https://github.com/KimHyeon-Ji/CSGR-Bio.git