# SSVEP · 8 通道（BETA）

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: 40 visual targets · 8 posterior electrodes
- 评测方式: 迁移到新被试
- 数据集: [BETA](https://bci.report/zh/datasets/beta/)
- 被试: 70
- 数据量: 11,200 trials · 7 participant-disjoint folds
- 输入: 8 channels selected from a 64-channel recording · 2-second windows
- 随机水平: 2.5%
- 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。

- Standard CCA: 63.1% (57.2%–69.0%)
- Spectral ridge: 50.7% (45.6%–55.8%)
- LaBraM: 10.8% (9.5%–12.2%)
- CBraMod: 33.7% (29.8%–37.6%)
- EEGNet: 55.8% (50.1%–61.3%)

| 方法 | 训练方式 | 平衡准确率 | 宏平均 F1 | 通道 | 被试 | 评分耗时 |
| --- | --- | --- | --- | --- | --- | --- |
| [Standard CCA](https://bci.report/zh/methods/cca/) — 经典方法 | Known-frequency reference · no fitting | 63.1% (57.2%–69.0%) | 0.626 — Mean across held-out participants | 8 | 70 | 1.8 s |
| Spectral ridge — 经典方法 | Supervised fit | 50.7% (45.6%–55.8%) | 0.494 — Mean across held-out participants | 8 | 70 | 1.4 s |
| [LaBraM](https://bci.report/zh/methods/labram/) — 基础模型 | Frozen encoder + ridge head | 10.8% (9.5%–12.2%) | 0.093 — Mean across held-out participants | 8 | 70 | 5.0 s |
| [CBraMod](https://bci.report/zh/methods/cbramod/) — 基础模型 | Frozen encoder + ridge head | 33.7% (29.8%–37.6%) | 0.318 — Mean across held-out participants | 8 | 70 | 4.9 s |
| [EEGNet](https://bci.report/zh/methods/eegnet/) — 轻量模型 | Scratch · 20 epochs | 55.8% (50.1%–61.3%) | 0.541 — Mean across held-out participants | 8 | 70 | 285.8 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/), 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

Near-floor scores are not ordered reliably between the 8- and 4-electrode subsets. Electrode subsets from laboratory recordings do not validate a physical low-channel cap. Prompted SSVEP does not measure idle false activations. Single-seed results; pretraining overlap unknown.

### 协议步骤

- Seven participant-disjoint folds: train on 60 people, test on ten. All four blocks stay with their participant.
- Two seconds from stimulus onset; no visual-latency shift. Source data were already zero-phase filtered. Additional 6–80 Hz filtering applies to each selected window separately.
- Microvolt units are inferred from an independently documented loader, not explicitly stated in the author MAT description. Inconsistent phase metadata are unused by all methods.
- Standard CCA uses known frequencies and three harmonics without training labels. Frozen encoders use training-only standardized ridge heads (alpha 100). EEGNet trains from scratch for 20 epochs with one seed (20260912).
- Electrodes: PO5, PO3, POZ, PO4, PO6, O1, OZ, O2.
- Uniform-guessing reference: 2.5%. Descriptive 95% intervals resample participants; training sets overlap across folds.
- No cross-task overall ranking, model fine-tuning optimum or hardware benchmark is claimed.

### 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

- **Standard CCA**：Trained from scratch / deterministic reference; no third-party pretrained weights. Braindecode BSD-3-Clause; MNE/scikit-learn BSD where used.
- **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

### BETA

**署名** Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA.

**许可** [CC BY 4.0 ↗](https://creativecommons.org/licenses/by/4.0/)

[数据集记录 ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) · [这个数据集上的全部结果 →](https://bci.report/zh/datasets/beta/)

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/beta-8ch-protocol.json)

**公开数据登记说明** 仅在本站声明的个人、以研究为主、非商业的运营方式下可以使用。任何广告、赞助、收费或以商业利益为目的的使用，都需要重新审查或取得许可。 Eligible only under the stated personal, research-led, noncommercial operation. Any ads, sponsorship, fees, or use directed toward commercial advantage requires fresh review or permission.

**权利审查于** 2026-09-20 · 依据 [figshare.com ↗](https://figshare.com/articles/dataset/The_BETA_database/12264401) · [doi.org ↗](https://doi.org/10.3389/fnins.2020.00627)

## 复现记录

- 协议 ID: `beta-ssvep-2s-posterior4and8-subject7fold-v1/posterior8`
- 数据集版本: Hugging Face mirror of BETA database, Figshare record 12264401 v3 · mirror d4290c0200db8a104e0f557349dc49f90ba79506 · upstream Figshare version 3, 2022-06-15
- 硬件: Apple M5 / MPS and CPU
- 评分阶段总耗时: 298.8 s · 可能含加速器等待
- 审计记录 SHA-256: `ccddf911864b8471f488e46cae45a3651369dae1134afcdeaede68bdfcc31950`
- 汇总 SHA-256: `547a35763f7bca10a65c7b44c2f8c46af290159c692d237e92cd682fc21127a6`
- 协议 SHA-256: `dcd1d2bf4dbb46b7db8c4562623448633cde4641fa372c90f25ba9d84012c2f0`

## 下载

- [结果 · CSV ↓](https://bci.report/data/beta-8ch-results.csv)
- [协议 · JSON ↓](https://bci.report/data/beta-8ch-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 · 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/beta-8ch/ 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）。