# SleepFM

Sleep foundation models compress multi-second EEG into compact tokens, leaving too little temporal detail to trace the onset and offset of sleep spindles. One approach keeps the SleepFM encoder frozen and adds a local time-frequency branch that preserves 11–16 Hz sigma-band energy and intra-token position, then uses residual cross-attention to bring in pretrained temporal context. On the MODA benchmark across six folds, event-level F1 is 0.8080±0.0040 at IoU 0.2 and 0.7766±0.0050 at IoU 0.5, with a faster decline at stricter boundary overlap.

Sleep foundation models compress multi-second EEG into compact tokens, leaving too little temporal detail to trace the onset and offset of sleep spindles. One approach keeps the SleepFM encoder frozen and adds a local time-frequency branch that preserves 11–16 Hz sigma-band energy and intra-token position, then uses residual cross-attention to bring in pretrained temporal context. On the MODA benchmark across six folds, event-level F1 is 0.8080±0.0040 at IoU 0.2 and 0.7766±0.0050 at IoU 0.5, with a faster decline at stricter boundary overlap.