# 运动想象与静息（ds003810）

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: Rest versus right-hand imagery
- 评测方式: 迁移到新被试
- 数据集: [ds003810](https://bci.report/zh/datasets/ds003810/)
- 被试: 10
- 数据量: 1,200 epochs · 5 participant-disjoint folds
- 输入: 15 channels · 2-second windows
- 随机水平: 50.0%
- 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: 54.9% (53.2%–56.7%)
- CSP+LDA: 47.1% (43.8%–50.2%)
- LaBraM: 53.5% (51.4%–55.4%)
- CBraMod: 62.9% (59.6%–66.5%)
- EEGNet: 71.0% (65.0%–76.3%)

| 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 | 54.9% (53.2%–56.7%) | 0.544 — Mean across held-out participants | 15 | 10 | 0.2 s |
| [CSP+LDA](https://bci.report/zh/methods/csp-lda/) — 经典方法 | Supervised fit | 47.1% (43.8%–50.2%) 不高于随机水平 | 0.406 — Mean across held-out participants | 15 | 10 | 8.8 s |
| [LaBraM](https://bci.report/zh/methods/labram/) — 基础模型 | Frozen encoder + ridge head | 53.5% (51.4%–55.4%) | 0.509 — Mean across held-out participants | 15 | 10 | 2.2 s |
| [CBraMod](https://bci.report/zh/methods/cbramod/) — 基础模型 | Frozen encoder + ridge head | 62.9% (59.6%–66.5%) | 0.617 — Mean across held-out participants | 15 | 10 | 5.0 s |
| [EEGNet](https://bci.report/zh/methods/eegnet/) — 轻量模型 | Scratch · 20 epochs | 71.0% (65.0%–76.3%) 仅单个随机种子——为已运行种子中最高的一次（见「稳定性」） | 0.680 — Mean across held-out participants | 15 | 10 | 55.5 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：** [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

Ten-person laboratory task with prompted rest. This is not continuous-idle monitoring or a physical low-channel headset test.

### 协议步骤

- 5 participant-disjoint folds. All recordings from a person stay together. Each person contributes to the held-out predictions once.
- epoching: two seconds from the class annotation onset; amplitude: converted to microvolts according to source calibration, then per-channel epoch mean removed; resampling: none; native sampling rate retained; selection: natural file/event order; when over the cap, retain 60 evenly spaced event indices per participant/class; source_units: Microv declared by BIDS channels.tsv; EDF physical dimension is absent, so MNE returns the source numeric microvolt values without SI scaling
- One fixed seed (20260919); no early stopping or test-based tuning. EEGNet trains for 20 epochs per fold. Frozen encoders use training-only standardized ridge heads (alpha 100).
- Labels: rest, right_hand_imagery.
- Uniform-guessing reference: 50.00%. Scores weight participants equally. Intervals describe participant variation; cross-validation training sets overlap.
- run 0 is real dominant-hand movement and is excluded; only imagery runs 1-4 contribute
- the source reports online 0.5-45 Hz filtering
- controlled cue-locked laboratory windows; this does not measure continuous false activations
- foundation-model pretraining overlap is unknown
- EEGNet three-seed mean 69.47%; sample SD 1.43 percentage points; range 68.17–71.00%. Main table retains the original fixed seed; this is not a confidence interval.

### 稳定性

EEGNet three-seed mean 69.47%; sample SD 1.43 percentage points; range 68.17–71.00%. Main table retains the original fixed seed; this is not a confidence interval.

EEGNet 各随机种子的结果： 71.00% · 69.25% · 68.17%；均值 69.47%

Same participants, folds, preprocessing and 20-epoch budget. Three seeds measure initialization variability, not population uncertainty. Main table retains its preselected seed; no best-seed selection.

### 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.
- 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. EEGNet three-seed mean 69.47%; sample SD 1.43 percentage points; range 68.17–71.00%. Main table retains the original fixed seed; this is not a confidence interval.

### 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.
- **CSP+LDA**：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

### ds003810

**署名** Peterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/

**数据集作者要求引用：** Peterson V, Galván C, Hernández H, Spies R. A feasibility study of a complete low-cost consumer-grade brain-computer interface system. Heliyon 6(3):e03425 (2020). [doi ↗](https://doi.org/10.1016/j.heliyon.2020.e03425) · [数据集记录中的要求 ↗](https://openneuro.org/datasets/ds003810)

**许可** [CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/)

[数据集记录 ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) · [这个数据集上的全部结果 →](https://bci.report/zh/datasets/ds003810/)

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。 [完整的审查说明在协议 JSON 中 ↓](https://bci.report/data/mi-rest-protocol.json)

**公开数据登记说明** CC0 快照明确，另有该数据集专属的伦理批准与签署同意书证据；只发布聚合结果，明显限制了隐私暴露。 Clear CC0 snapshot plus dataset-specific ethics and signed-consent evidence; aggregate-only publication materially limits privacy exposure.

**权利审查于** 2026-09-20 · 依据 [doi.org ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2) · [pmc.ncbi.nlm.nih.gov ↗](https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/)

**已登记的更正 · 2026-10-01**：ds003810 的署名只链接了 2022 年的 Data in Brief 数据描述。OpenNeuro 记录要求引用 Peterson、Galván、Hernández 与 Spies 发表于 Heliyon 6(3):e03425（2020）的论文；数据集页面现在两者都给出。 [更正登记册 →](https://bci.report/zh/releases/#corrections)

## 复现记录

- 协议 ID: `parallel-fixed-subject-folds-v1/ds003810`
- 数据集版本: OpenNeuro snapshot 2.0.2 · c674303319303b9ffc4ff7e2340b3a4807e8ffb5
- 硬件: Apple M5 / MPS
- 评分阶段总耗时: 71.7 s · 可能含加速器等待
- 审计记录 SHA-256: `e762cf7f048bf2df47b9399b9df6171733a93c162ce7800560952df30491a7f2`
- 汇总 SHA-256: `0dd66f4bd4d083fca1d1ab44a2d2951d57f6d19378079b8cb5ff3ee9241f0cda`
- 协议 SHA-256: `57ddb7692e3bb248b95c4e81b6aa02e7c8b19d774ed4a8b799c811b6ef3b65a2`

## 下载

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

### 其他协议

- [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/)
- [睡眠分期（EESM19 scalp subset）](https://bci.report/zh/protocols/sleep-scalp/)

[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/mi-rest/ 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）。