BCI ReportP300 target ERP
The cBCI Matrix Multimodal Dataset (OpenNeuro ds006593) contains EEG and eye-tracking calibration recordings from 21 control participants performing a single-character-presentation matrix speller task in BciPy, recorded at Northeastern University in 2023 with a DSI-24 dry-electrode cap. Released on OpenNeuro in 2025 by Celik et al.
Also known as ds006593 · cBCI Matrix Multimodal Dataset · Multimodal Sensor Fusion for EEG-Based BCI Typing Systems · on006593
Description sources openneuro.org · nemar.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 | Visual target versus nontarget events | Balanced accuracy | 51.0%49.6%–52.4% | 21 |
| Temporal ridge | Visual target versus nontarget events | Balanced accuracy | 53.8%51.9%–55.6% | 21 |
| LaBraM | Visual target versus nontarget events | Balanced accuracy | 49.4%47.7%–51.2% | 21 |
| CBraMod | Visual target versus nontarget events | Balanced accuracy | 55.0%53.3%–56.7% | 21 |
| EEGNet | Visual target versus nontarget events | Balanced accuracy | 53.3%51.7%–54.9% | 21 |
| XDAWNCov+TS+LR · 4 filters · 50 Hz | Visual target versus nontarget events | Balanced accuracy | 61.9%58.8%–65.0%Primary row · one fixed configuration (MOABB-reference settings) | 21 |
| xDAWN+LDA · 4 filters · 50 Hz | Visual target versus nontarget events | Balanced accuracy | 60.4%57.7%–63.2%Secondary row · one fixed configuration (MOABB-reference settings) | 21 |
| ShallowFBCSPNet | Visual target versus nontarget events | Balanced accuracy | 49.7%49.0%–50.4%One fixed seed and training budget, as the EEGNet cell | 21 |
| Deep4Net | Visual target versus nontarget events | Balanced accuracy | 50.8%50.0%–51.6%Braindecode's automatically scaled Deep4Net for 1-s input (no temporal pooling, 229-sample classifier, 235,752 parameters), not the original Deep4Net configuration. The interval's lower end, 50.04 %, prints as 50.0; it sits 0.04 pp above the 50 % reference. | 21 |
| CSP+LDA | Visual target versus nontarget events | Balanced accuracy | 50.0%49.1%–50.8%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. | 21 |
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| REVE Base | Frozen encoder + ridge head | Balanced accuracy | 50.5%48.5%–52.3% | 21 |
| REVE Large | Frozen encoder + ridge head | Balanced accuracy | 54.6%52.7%–56.5% | 21 |
| LUNA Base | Frozen encoder + ridge head | Balanced accuracy | 48.7%46.3%–51.1% | 21 |
| LUNA Large | Frozen encoder + ridge head | Balanced accuracy | 48.8%46.9%–50.8% | 21 |
| CodeBrain | Frozen encoder + ridge head | Balanced accuracy | 50.8%49.3%–52.2% | 21 |
| EEGMamba | Frozen encoder + ridge head | Balanced accuracy | 52.5%51.4%–53.5% | 21 |
| ST-EEGFormer Base | Frozen encoder + ridge head | Balanced accuracy | 53.6%52.3%–54.8% | 21 |
| ST-EEGFormer Large | Frozen encoder + ridge head | Balanced accuracy | 52.7%51.3%–54.2% | 21 |
| eeg-fm-masking MAE (r=9 cm, L=2) | Frozen encoder + ridge head | Balanced accuracy | 49.5%48.1%–51.0% | 21 |
| ERP-FM Base | Frozen encoder + ridge head | Balanced accuracy | 51.2%49.3%–53.2% | 21 |
| SingLEM | Frozen encoder + ridge head | Balanced accuracy | 51.2%49.2%–53.1% | 21 |
| ZUNA 1.1 | Frozen encoder + ridge head | Balanced accuracy | 51.6%50.3%–53.0%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. | 21 |
| eeg-fm-masking JEPA (r=9 cm, L=2) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 51.6%50.4%–52.7% | 21 |
| eeg-fm-masking MAE (r=one channel, L=1) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 51.1%49.2%–53.0% | 21 |
| eeg-fm-masking JEPA (r=one channel, L=1) | Frozen encoder + ridge head · masking ablation | Balanced accuracy | 50.0%48.7%–51.5% | 21 |
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 | 54.0%51.5%–56.6% | 21 |
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 OpenNeuro ds006593 contributors · version 1.0.0, doi:10.18112/openneuro.ds006593.v1.0.0; original author credits retained at the linked source.
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). ds006593: EEG decoding results. https://bci.report/datasets/ds006593/
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 ↗