BCI ReportSemantic target ERP
TMNRED is a Chinese natural-reading EEG dataset from Tianjin University: 30 native Chinese-speaking participants read news headlines or short sentences containing target or non-target semantic items across 8 blocks, recorded with a portable 32-channel system (30 effective channels). Released on OpenNeuro (ds005383) and described by Bai et al. in Scientific Data (2025).
Also known as TMNRED · ds005383 · TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments · Bai et al. (2025)
Description sources openneuro.org · pmc.ncbi.nlm.nih.gov · doi.org
Each figure comes from the reviewed download linked in its group. One row is above another only where their 95% intervals do not overlap.
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Spectral ridge | Reading · target versus nontarget events | Balanced accuracy | 51.0%49.6%–52.5% | 30 |
| Temporal ridge | Reading · target versus nontarget events | Balanced accuracy | 56.3%54.4%–58.4% | 30 |
| LaBraM | Reading · target versus nontarget events | Balanced accuracy | 53.2%51.3%–54.9% | 30 |
| CBraMod | Reading · target versus nontarget events | Balanced accuracy | 55.8%54.2%–57.4% | 30 |
| EEGNet | Reading · target versus nontarget events | Balanced accuracy | 61.4%58.9%–64.0% | 30 |
| XDAWNCov+TS+LR · 4 filters · 50 Hz | Reading · target versus nontarget events | Balanced accuracy | 60.3%58.2%–62.6%Primary row · one fixed configuration (MOABB-reference settings) | 30 |
| xDAWN+LDA · 4 filters · 50 Hz | Reading · target versus nontarget events | Balanced accuracy | 56.8%55.1%–58.6%Secondary row · one fixed configuration (MOABB-reference settings) | 30 |
| ShallowFBCSPNet | Reading · target versus nontarget events | Balanced accuracy | 56.7%54.7%–58.8%One fixed seed and training budget, as the EEGNet cell | 30 |
| Deep4Net | Reading · target versus nontarget events | Balanced accuracy | 57.4%55.4%–59.5%Braindecode's automatically scaled Deep4Net for 1-s input (no temporal pooling, 229-sample classifier, 242,627 parameters), not the original Deep4Net configuration. | 30 |
| CSP+LDA | Reading · target versus nontarget events | Balanced accuracy | 52.7%51.4%–54.0%Broadband CSP without band-pass filtering, the recipe of the published mi-rest CSP+LDA cell; not a tuned CSP. On this ERP protocol it reads band power, not the waveform. | 30 |
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| REVE Base | Frozen encoder + ridge head | Balanced accuracy | 53.7%51.8%–55.4% | 30 |
| REVE Large | Frozen encoder + ridge head | Balanced accuracy | 59.7%57.5%–61.8% | 30 |
| LUNA Base | Frozen encoder + ridge head | Balanced accuracy | 56.2%54.2%–58.2% | 30 |
| LUNA Large | Frozen encoder + ridge head | Balanced accuracy | 57.1%55.3%–58.9% | 30 |
| CodeBrain | Frozen encoder + ridge head | Balanced accuracy | 54.7%53.0%–56.6% | 30 |
| EEGMamba | Frozen encoder + ridge head | Balanced accuracy | 54.4%52.5%–56.3% | 30 |
| ST-EEGFormer Base | Frozen encoder + ridge head | Balanced accuracy | 54.6%52.9%–56.4% | 30 |
| ST-EEGFormer Large | Frozen encoder + ridge head | Balanced accuracy | 57.8%56.0%–59.6% | 30 |
| eeg-fm-masking MAE (r=9 cm, L=2) | Frozen encoder + ridge head | Balanced accuracy | 54.8%52.7%–56.9% | 30 |
| ERP-FM Base | Frozen encoder + ridge head | Balanced accuracy | 53.3%51.7%–54.8% | 30 |
| SingLEM | Frozen encoder + ridge head | Balanced accuracy | 58.5%56.1%–61.1%Pretraining exposure: in the authors' published pretraining list. | 30 |
| ZUNA 1.1 | Frozen encoder + ridge head | Balanced accuracy | 53.9%52.2%–55.6%Pretraining exposure: unknown: the authors do not list their pretraining data closely enough to decide. Model card: research use only, not for diagnosis or clinical use. | 30 |
| eeg-fm-masking JEPA (r=9 cm, L=2) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 55.8%53.9%–57.6% | 30 |
| eeg-fm-masking MAE (r=one channel, L=1) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 54.9%53.4%–56.4% | 30 |
| eeg-fm-masking JEPA (r=one channel, L=1) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 55.0%53.4%–56.6% | 30 |
Weights terms: REVE under the REVE Responsible Use License v1.0 (model versions: REVE Base @ dc2a075c · REVE Large @ 317531c7); LUNA under CC BY-ND 4.0, with no modified weights shared; CodeBrain and ZUNA under Apache-2.0; EEGMamba, ST-EEGFormer and SingLEM under MIT; eeg-fm-masking under CC-BY-4.0; ERP-FM under CC BY-NC-SA 4.0, non-commercial. No model’s authors endorse these results. Every weights licence and its terms →
BrainOmni Base: not run. BrainTokenizer needs a 512-sample window (2.0 s at 256 Hz); the published segment is 1 s = 256 samples, so upstream unfold() would zero-pad 256 of 512 samples of every window (invented samples). All upstream downstream tasks use >= 2 s windows.
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| EEGPT | Frozen encoder + ridge head | Balanced accuracy | 54.6%53.3%–55.9% | 30 |
On the P300 and semantic targets, one-second windows ran as four real 0.25-second patches: the patch grid was set to four after the encoder was built, with no padding. EEGPT’s encoder treats each time patch on its own, so this is numerically the same as building it for 256-sample inputs, as the Orthosis pilot built it for its 128-sample windows.
Every core window is shorter than EEGPT’s 4-second pretraining window: 1 or 2 seconds, and sleep as fifteen 2-second segments. The encoder computes each 0.25-second patch the same way at any length; a shorter window gives fewer patches to average.
The readout is the site’s fixed probe on the frozen summary tokens, as for the published LaBraM and CBraMod rows: ridge (alpha 100) on 2,048 pooled values, and on idle a per-person logistic head. It is not the authors’ downstream probe, a learnable spatial filter with a linear head, and the windows are fed without the average reference EEGPT was pretrained with.
EEGPT: the checkpoint’s licence is unresolved, and no original or converted weights are distributed; Apache-2.0 covers the repository’s code only. Its licence and terms →
Credit 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.
OpenNeuro metadata says CC0; accompanying publication/GitHub says CC BY 4.0 ↗ · Dataset record ↗
BCI Report does not redistribute any recording. These are aggregate measurements computed by BCI Report under the licence above; the data remain their creators’.
BCI Report (2026). TMNRED / ds005383: EEG decoding results. https://bci.report/datasets/tmnred/
Figures from releases core-matrix-update-20261106 (2026-11-06), eegpt-core-update-20261013 (2026-10-13), foundation-models-update-20261004 (2026-10-04), research-preview-20260920 (2026-09-20). Cite the upstream dataset as well: its credit is on this page.
Every release is archived on Zenodo: doi:10.5281/zenodo.23123296. BibTeX for the site and its releases → · CITATION.cff ↗