Frequency-Aware Contrastive Loss for Self-Supervised EEG Seizure Detection

Contrastive Representation Learning (CRL), a sub-field of self-supervised learning, models relationships between data points by comparing pairs of samples and utilizing specifically formulated contrastive pretext tasks in which multiple views of each input example are generated via data augmentation. While contrastive learning has shown promise in EEG analysis, current methods typically rely on random negative sampling, which may not capture the subtle distinctions crucial for seizure detection. Our proposed method, Frequency-aware NT-Xent (FA-NT-Xent) loss, introduces a principled approach to hard negative selection by leveraging domain knowledge from EEG analysis. Specifically, we construct a distance matrix between batch samples based on their spectral band power profiles across five canonical frequency bands (delta, theta, alpha, beta, gamma), enabling the identification of samples that share similar frequency characteristics but belong to different classes. These hard negative examples are weighted by a learnable parameter and integrated into the contrastive loss computation, enhancing the model's ability to distinguish between physiologically similar but clinically distinct patterns. Evaluated on the CHB-MIT dataset using a cross-patient validation scheme, our method demonstrates improvements over the standard NT-Xent loss and achieves competitive performance against state-of-the-art approaches. Visualization of the learned representations reveals improved class separation and the emergence of patient-independent seizure characteristics, indicating that our frequency-aware approach successfully captures clinically relevant features while maintaining robust cross-patient generalization.