Balanced quality-screened scalp subset, not whole-night deployment prevalence and not ear-EEG. Frozen encoders average fifteen 2-second representations; EEGNet receives 10 epochs.
协议步骤
5 participant-disjoint folds. All recordings from a person stay together. Each person contributes to the held-out predictions once.
Retain six named scalp electrodes; exclude mastoids. Drop any epoch with source per-channel missing-value flag in those six electrodes, actual nonfinite values, any channel std<0.01uV, or peak-to-peak>1000uV. Use full30s at200Hz, no further filtering/reference/amplitude transformation. Select up to30 evenly spaced eligible epochs per participant/class across available nights.
One fixed seed (20260919); no early stopping or test-based tuning. EEGNet trains for 10 epochs per fold. Frozen encoders use training-only standardized ridge heads (alpha 100).
Lightweight balanced quality-screened subset; scores do not describe natural sleep-stage prevalence or the entire73780epoch release.
Two original corrupt sessions and boundary epochs were excluded by the uploader.
200Hz data may have finite replacements despite original missing-value flags; source flags are therefore enforced instead of relying on finite checks alone.
One prediction per30s epoch. Frozen encoders pool15nonoverlapping2s segments; no sequence context across epochs.
All nights from a participant remain together; pretraining overlap unknown.
Quality thresholds fixed before any scores; source artifacts may remain.
稳定性
One fixed seed and training budget; multi-seed sensitivity pending.
是否出现在预训练数据中
Unknown unless explicitly documented; no unseen-pretraining claim.
方法说明
One fixed configuration. Foundation encoders remain frozen; small networks train from scratch. These scores do not establish optimal fine-tuned performance. No individual predictions or participant-level results are distributed.
模型条款
Spectral ridge：Trained from scratch / deterministic reference; no third-party pretrained weights. Braindecode BSD-3-Clause; MNE/scikit-learn BSD where used.
LaBraM：Code/repository: MIT · Checkpoint: committed in that repository; no separate weight terms
CBraMod：Code: MIT · Weights: Apache-2.0 (official model card)
EEGNet：Trained from scratch / deterministic reference; no third-party pretrained weights. Braindecode BSD-3-Clause; MNE/scikit-learn BSD where used.
来源与许可
EESM19 scalp subset
署名Kaare B. Mikkelsen et al. · Accurate whole-night sleep monitoring with dry-contact ear-EEG (2019), doi:10.1038/s41598-019-53115-3; OpenNeuro ds005185 v1.0.2. Processed mirror: Zachary1150/EESM19-Processed.
公开数据登记说明上游 CC0 明确，研究伦理证据清楚；只使用锁定版本的头皮信号衍生数据，只发布聚合结果，并说明来源。同意书覆盖的是研究本身而不是公开发布；数据的公开依据是一项 GDPR 匿名化评估（2026-09-22 修订）。Clear upstream CC0 and study ethics evidence; use only the pinned scalp-signal derivative and publish aggregates, with provenance disclosed. Consent covered the study, not publication; the public release rests on a GDPR anonymization assessment (amended 2026-09-22).
BCI Report