# 睡眠分期（EESM19 scalp subset）

核心矩阵协议 · 迁移到新被试

下面这些分数是怎么来的——队列、数据划分、电极、时间窗、每种方法允许学什么、随机猜测能得多少分——都按已发布的协议文件给出。只在本协议内部比较分数。

协议步骤、局限说明、方法备注与数据集署名来自发布数据本身，保持英文原文——它们随数据一起被审核，翻译会让网页与可下载文件不再一致。

## 协议概览

- 任务: Five stages · balanced scalp-EEG sample
- 评测方式: 迁移到新被试
- 数据集: [EESM19 scalp subset](https://bci.report/zh/datasets/eesm19/)
- 被试: 20
- 数据量: 3,000 epochs · 5 participant-disjoint folds
- 输入: 6 channels · 30-second windows
- 随机水平: 20.0%
- 指标: 平衡准确率（主指标）· 宏平均 F1（次指标）

## 结果

在本协议下运行过的每一种方法，以及已发布结果文件中记录的分数。只在这张表内纵向比较：其他协议在队列、电极、时间窗或随机水平上有所不同。

**平衡准确率，本协议下的每一种方法。** 点为估计值，横线为描述性 95% 区间，虚线为随机水平。

- Spectral ridge: 72.2% (70.7%–73.8%)
- LaBraM: 71.1% (69.0%–73.2%)
- CBraMod: 71.3% (68.7%–73.7%)
- EEGNet: 56.5% (54.9%–58.3%)

| 方法 | 训练方式 | 平衡准确率 | 宏平均 F1 | 通道 | 被试 | 评分耗时 |
| --- | --- | --- | --- | --- | --- | --- |
| Spectral ridge — 经典方法 | Supervised fit | 72.2% (70.7%–73.8%) | 0.705 — Mean across held-out participants | 6 | 20 | 2.6 s |
| [LaBraM](https://bci.report/zh/methods/labram/) — 基础模型 | Frozen encoder + ridge head | 71.1% (69.0%–73.2%) | 0.697 — Mean across held-out participants | 6 | 20 | 11.5 s |
| [CBraMod](https://bci.report/zh/methods/cbramod/) — 基础模型 | Frozen encoder + ridge head | 71.3% (68.7%–73.7%) | 0.702 — Mean across held-out participants | 6 | 20 | 10.2 s |
| [EEGNet](https://bci.report/zh/methods/eegnet/) — 轻量模型 | Scratch · 10 epochs | 56.5% (54.9%–58.3%) | 0.545 — Mean across held-out participants | 6 | 20 | 90.4 s |

评分耗时包括拟合与预测，可能含加速器等待时间，不含数据准备。它只对该配置有效，不是硬件基准。

**未在本协议下运行：** [CSP+LDA](https://bci.report/zh/methods/csp-lda/), [ShallowFBCSPNet](https://bci.report/zh/methods/shallowfbcspnet/), [Deep4Net](https://bci.report/zh/methods/deep4net/), [Standard CCA](https://bci.report/zh/methods/cca/), Temporal ridge。这里缺少的方法只是没有在本协议上运行——不是失败。

## 谨慎解读

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).
- Labels: Wake, N1, N2, N3, REM.
- Uniform-guessing reference: 20.00%. Scores weight participants equally. Intervals describe participant variation; cross-validation training sets overlap.
- 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.

**许可** [CC0-1.0（上游与镜像均如此声明） ↗](https://creativecommons.org/publicdomain/zero/1.0/) CC0-1.0 declared by upstream and mirror

[数据集记录 ↗](https://doi.org/10.18112/openneuro.ds005185.v1.0.2) · [这个数据集上的全部结果 →](https://bci.report/zh/datasets/eesm19/)

BCI Report 不转发任何记录。这些是 BCI Report 在上述许可下计算的聚合测量；数据归属于署名中的作者。

**许可范围** Personal noncommercial research; aggregate results only

**隐私** 这里只发布队列级聚合结果：不发布任何记录、被试编号或逐人分数。 据 2025 年的数据描述论文，知情同意书没有提到公开发布；发布前，Region Midt（丹麦中部大区）的 GDPR 办公室判定这些数据已完全匿名化：同意书覆盖的是研究本身，公开发布依据的是这一匿名化判定。 [完整的审查说明在协议 JSON 中 ↓](https://bci.report/data/sleep-scalp-protocol.json)

**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).

**权利审查于** 2026-09-20 · 依据 [doi.org ↗](https://doi.org/10.18112/openneuro.ds005185.v1.0.2) · [www.nature.com ↗](https://www.nature.com/articles/s41598-019-53115-3) · [doi.org ↗](https://doi.org/10.1038/s41597-025-04579-8)

## 复现记录

- 协议 ID: `parallel-fixed-subject-folds-v1/eesm19-scalp-sleep`
- 数据集版本: Hugging Face processed mirror of OpenNeuro ds005185 1.0.2 · mirror fb045012b58a0b755f9fedf5931dff9b64a9797f · upstream 0857858f7a2ba1582930f23eca3ec56f90a96da9
- 硬件: Local Ubuntu / CUDA
- 评分阶段总耗时: 114.7 s · 可能含加速器等待
- 审计记录 SHA-256: `e6cbf1b199bf2902b9c46e4575ebfa8aff47952625df3d71a1d720b11cc27291`
- 汇总 SHA-256: `b7c784c17d6b33c16ffca5df42d3fd8f3d7bf14960675a7278089c4903886567`
- 协议 SHA-256: `dace8a98e480ae7ee890dd6f498d5753a90e1573813b6e174f3c14bcbae8a8ff`

## 下载

- [结果 · CSV ↓](https://bci.report/data/sleep-scalp-results.csv)
- [协议 · JSON ↓](https://bci.report/data/sleep-scalp-protocol.json)
- [核心矩阵 · JSON ↓](https://bci.report/data/experiments.json)

### 其他协议

- [运动想象与静息（ds003810）](https://bci.report/zh/protocols/mi-rest/)
- [空闲与指令（ds005342）](https://bci.report/zh/protocols/idle/)
- [SSVEP · 8 通道（BETA）](https://bci.report/zh/protocols/beta-8ch/)
- [SSVEP · 4 通道（BETA）](https://bci.report/zh/protocols/beta-4ch/)
- [心算与静息（EEGMAT）](https://bci.report/zh/protocols/arithmetic-rest/)
- [P300 目标 ERP（ds006593）](https://bci.report/zh/protocols/p300-target/)
- [语义目标 ERP（TMNRED / ds005383）](https://bci.report/zh/protocols/semantic-target/)

[全部协议 →](https://bci.report/zh/protocols/) · [首页上的核心矩阵 →](https://bci.report/zh/#overview) · [全部数据集 →](https://bci.report/zh/datasets/) · [全部方法 →](https://bci.report/zh/methods/)

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本文是 https://bci.report/zh/protocols/sleep-scalp/ 的 Markdown 版本，由已发布的网页生成。数字均为聚合结果；使用条款见 https://bci.report/data-use/（英文）。