Contrastive self-supervised learning for epileptic seizure detection from EEG with calibration and robustness evaluation under physiology-aligned perturbations

Labelled EEG data for epileptic seizure detection are scarce, and clinical recordings routinely suffer from realistic signal degradation including electrode loss, baseline drift, and sampling jitter. We present a proof-of-concept study applying contrastive self-supervised learning (SSL) to EEG epileptic seizure detection, comparing a SimCLR-style InfoNCE encoder pre-trained on unlabelled EEG against a supervised-only baseline sharing the same backbone, and evaluating both through a framework encompassing discrimination, operating-point behaviour, calibration, and physiology-aligned robustness. Stratified random 60/20/20 splits were applied (the Kaggle CHB-MIT derivative lacks patient identifiers; subject leakage is acknowledged as a limitation). On the held-out test set (11,233 segments, 23 channels, 6.26 s at 256 Hz, 6.8:1 imbalance), the SSL model achieves AUROC 0.990 (95% CI: 0.985–0.994) versus 0.989 (0.983–0.994) for the baseline, with overlapping CIs confirming statistical comparability. At threshold 0.5, SSL achieves higher recall (0.951 [0.925, 0.975] versus 0.924 [0.891, 0.953]) at the cost of lower precision (0.669 [0.626, 0.713] versus 0.839 [0.799, 0.878]); non-overlapping CIs confirm the precision gap; the recall advantage has overlapping CIs; both carry alarm-fatigue implications. The baseline is better calibrated on both ECE (0.027 [0.021, 0.034] versus 0.066 [0.057, 0.075]) and Brier score. In robustness tests, SSL is more robust to sampling jitter (± 20%: 0.968 [0.960, 0.976] versus 0.960 [0.947, 0.970]) while the baseline is more robust to strong drift (amplitude 0.40: 0.970 [0.960, 0.979] versus 0.913 [0.894, 0.931]); the drift gap has non-overlapping CIs, the jitter gap marginally overlapping CIs; both trace to augmentation design. Findings inform SSL augmentation design for future EEG monitoring work.