SPAN: Set-Prediction Adaptation for Continuous Biosignal Event Decoding

Continuous biosignal applications, including seizure monitoring, sleep analysis and motor-imagery decoding, require predictions with an accurate class, onset and offset. Yet pretrained biosignal foundation models are almost universally adapted through per-window classifiers, whose outputs must be thresholded, smoothed and merged into events after the fact. We show that this adaptation interface, and not only the pretrained representation, is a decisive factor in event-level quality. We introduce SPAN, a temporal set-prediction framework that maps tokens from heterogeneous biosignal backbones into a shared physical-time representation and directly predicts event intervals via multiscale temporal features, anchor-conditioned queries and duration-dependent attention. Using a paired protocol that fixes the pretrained initialization, training data and event scorer, we compare SPAN against dense per-token fine-tuning (Dense-FT) across six public EEG foundation-model backbones, four tasks and three seeds. SPAN improves test event F1 at IoU$>0.5$ in all 72 seed-matched comparisons, with average gains of 4.5–8.3 percentage points per task, and reduces onset error on matched seizure, motor-imagery and arousal events. A further comparison against a plain set decoder on identical backbone features shows that these gains come from SPAN's combined design rather than set prediction alone. On densely overlapping artifact annotations, boundary localization and fragmentation remain difficult, pointing to a concrete direction for future work. Together, these results establish the adaptation interface as a substantial, previously underexamined lever for transferring pretrained biosignal representations to temporally localized event decoding, and position SPAN as a strong, broadly validated default for this setting.