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Spontaneous memory replay during sleep is crucial for cognition but challenging to capture because distinct sleep rhythms hinder the generalization of wake-trained electroencephalogram (EEG) decoders. To address this, we developed the Sleep Interpreter (SI), which uses neural contrastive learning to isolate shared semantic content from background rhythms. We collected a dataset of 135 participants undergoing targeted reactivation of 15 semantic categories, yielding approximately 1,000 h of overnight sleep and 400 h of wake EEG. During non-rapid eye movement (NREM) sleep, SI achieved high decoding accuracy for cue-evoked semantic responses, with accuracy peaking during slow oscillation and spindle coupling at 40.02% top-1 accuracy on unseen participants (chance 6.7%). We demonstrated SI generalizability in two independent nap experiments involving targeted and spontaneous reactivation, where decoded reactivations correlated with post-sleep memory performance. Finally, we implemented SI for real-time sleep staging and stage-specific NREM and REM decoding. The dataset and codebase are shared as open resources for future clinical applications.
SI achieved high decoding accuracy for cue-evoked semantic responses, with accuracy peaking during slow oscillation and spindle coupling at 40.02% top-1 accuracy on unseen participants (chance 6.7%)
phase-specific maximum after test-informed retraining on the same 12 held-out participants
SI-MD achieved zero-shot accuracies of 22.82% ± 0.52% (top 5: 64.84% ± 0.95%) in NREM2/3
participant-held-out performance supports the constrained classification result, with only 12 participants in the principal test set
decoding probability during SO-spindle coupling predicted subsequent retention of the learned associations
correlational validation in 25 participants supports an association rather than causal or individual-level predictive validation
Derived from the full evaluation — not a separate score.
Strengths
The study supplies wake and overnight EEG from 135 participants across 15 categories, with named locations for data, code, and pretrained models.
↳ Results, Task design and dataset; Resource Availability; STAR Methods, Key resources table
Off-the-shelf nap experiments use arbitrary non-semantic cues and a separate cue-free paradigm on different EEG hardware. These tests reduce the likelihood that performance depends only on the original acoustic cues.
↳ Results, Validation experiments 1–2; Figures 4–5; STAR Methods, Experiments 1 and 2
The limitations distinguish closed-set decoding from broader content interpretation and identify high-density EEG and healthy young participants as barriers to wider use.
↳ Discussion, limitations
Limitations
The sleep-only comparator removes wake data and associated encoders along with contrastive learning. Phase-enriched retraining also changes convolutional depth, preventing separate attribution of its improvement to data selection or architecture.
↳ STAR Methods, Decoding model comparison and ablation experiment; Model retrain
The 40.02% result is a phase-specific maximum on 12 held-out participants after phase-informed retraining. Overall retrained accuracy is 23.45%, and the peak lacks a fresh confirmatory participant set.
↳ Results, Sleep decoding as a function of SO phase and spindle coupling; Figure 3; STAR Methods, Model retrain
The validation cohorts remain small and demographically restricted, while semantic decoding requires high-density EEG and predefined categories. The real-time section measures latency but does not evaluate online semantic-decoding accuracy.
↳ Results, Validation experiments 1–2 and Real-time sleep-decoding system; Discussion
The open dataset, participant-held-out comparisons, and independent nap experiments support a meaningful resource-and-decoding contribution. The arbitrary-cue and cue-free validations materially strengthen interpretation beyond recognition of the original sounds. However, the main ablation does not separate contrastive alignment from access to wake data, and phase-enriched retraining changes both architecture and training composition. The 40.02% figure is a selected phase-specific peak rather than typical performance, while the real-time prototype establishes latency rather than validated online semantic accuracy.
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Strong3.8
Confidence mediumThe score reflects a substantial open EEG resource coupled to cross-participant decoding and independent task validations. The closed vocabulary and limited comparability with prior-work performance estimates constrain the breadth of the advance.
Participant-held-out evaluation, broad baselines, and independent off-the-shelf validation support the main decoding result. The score includes explicit reductions for non-isolated component changes and test-informed optimization of the phase-specific peak.
The manuscript makes its dataset-to-model-to-validation sequence followable and identifies the phase-specific nature of the headline accuracy. Broad statements about generalizability and removal of background rhythms are less tightly calibrated than the operational results.
The paper situates decoding within sleep-memory research and connects several scope limitations to future validation needs. Post-hoc peak selection and the cue-evoked contribution to phase effects are not fully carried through the interpretation.
Caveats4 of 4 checks
One reported value does not reconcile, but the key claims do not rest on it.
Ethics approval, written consent, no competing interests, and public locations for data, code, and pretrained models are declared. No preregistration is stated.
Conflict of interest: declared · Ethics approval: declared · Data availability: declared · Code availability: declared
123 references in manuscript 120 of 122 checkable references found in an index 1 are books, websites or datasets — counted, but not index-checkable 1 mistyped DOI matched to the cited work by its details
No retraction or expression of concern found in Crossref (includes Retraction Watch).
Sources: Crossref (incl. Retraction Watch) ✓
Where this paper’s evidence sits on the path from initial observation to real-world use.
Independent laboratory validations and a functioning real-time prototype establish technical feasibility beyond model development alone. Latency measurements do not establish online semantic accuracy or intervention benefit.
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