核心矩阵协议 · 迁移到新被试

语义目标 ERP（TMNRED / ds005383）

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: Reading · target versus nontarget events
评测方式: 迁移到新被试
数据集: TMNRED / ds005383
被试: 30
数据量: 3,600 epochs · 5 participant-disjoint folds
输入: 30 channels · 1-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。

Spectral ridge

51.0% (49.6%–52.5%)

Temporal ridge

56.3% (54.4%–58.4%)

LaBraM

53.2% (51.3%–54.9%)

CBraMod

55.8% (54.2%–57.4%)

EEGNet

61.4% (58.9%–64.0%)

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。

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	51.0%49.6%–52.5%区间触及随机水平	0.506Mean across held-out participants	30	30	0.9 s
Temporal ridge经典方法	Supervised fit	56.3%54.4%–58.4%	0.559Mean across held-out participants	30	30	1.2 s
LaBraM基础模型	Frozen encoder + ridge head	53.2%51.3%–54.9%	0.519Mean across held-out participants	30	30	3.4 s
CBraMod基础模型	Frozen encoder + ridge head	55.8%54.2%–57.4%	0.554Mean across held-out participants	30	30	23.5 s
EEGNet轻量模型	Scratch · 20 epochs	61.4%58.9%–64.0%	0.606Mean across held-out participants	30	30	83.4 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, ShallowFBCSPNet, Deep4Net, Standard CCA。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 event subset with collapsed semantic categories. Natural class prevalence is not preserved. Third-party reading passages and participant metadata are excluded from this website.

协议步骤

5 participant-disjoint folds. All recordings from a person stay together. Each person contributes to the held-out predictions once.
units: microvolts; epoch: [event onset, onset + 1.0 s); filtering: none added
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: nontarget, target.
Uniform-guessing reference: 50.00%. Scores weight participants equally. Intervals describe participant variation; cross-validation training sets overlap.
Research subset with fixed deterministic sampling capped at 60 epochs per participant/class.
Balancing to 60 per class changes the source target/nontarget prevalence.
Events use numbered target/nontarget variants; the benchmark collapses the suffix only because the trial_type prefix explicitly names the semantic class.

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.
Temporal 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

TMNRED / ds005383

署名 Yanru Bai, Qi Tang et al. · TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments (2025), doi:10.1038/s41597-025-05036-2. OpenNeuro ds005383 v1.0.0.

许可 OpenNeuro 元数据为 CC0；配套论文与 GitHub 为 CC BY 4.0 ↗OpenNeuro metadata says CC0; accompanying publication/GitHub says CC BY 4.0

数据集记录 ↗ · 这个数据集上的全部结果 →

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 ↓

公开数据登记说明 有充分的、该研究专属的公开共享证据。聚合指标不会再分发刺激文本与被试元数据；按 CC BY 署名。Strong study-specific open-sharing evidence. Aggregate metrics avoid redistribution of stimulus text and participant metadata; credit under CC BY.

权利审查于 2026-09-20 · 依据 doi.org ↗ · www.nature.com ↗

复现记录

协议 ID: parallel-fixed-subject-folds-v1/ds005383
数据集版本: OpenNeuro snapshot 1.0.0 · ad78f3db430e636595b1b1c08417492f3067a25e
硬件: Local Ubuntu / CUDA
评分阶段总耗时: 112.4 s · 可能含加速器等待
审计记录 SHA-256: 5920974ae9ca3876ce1be82497c139f0783d2ff47173641e329af976441de4c4
汇总 SHA-256: c5dca7fc30d07a001b8980a4ea65e55ef292f0891a4d32a12262b8ec88389243
协议 SHA-256: 839dfba89cf200aac7a28a7259f45024d006fcd4500170e247fcc1c4e9962c8d

下载

结果 · CSV ↓协议 · JSON ↓核心矩阵 · JSON ↓

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