OpenNeuro ds003810 is an EEG dataset of kinesthetic motor imagery of dominant-hand grasping versus rest, recorded from 10 participants without previous BCI experience using a low-cost OpenBCI Cyton + Daisy board with 15 electrodes over sensorimotor areas. Released on OpenNeuro by Peterson et al. and described in Data in Brief (2022).
Also known asds003810 · Motor Imagery vs Rest - Low-Cost EEG System · A motor imagery vs. rest dataset with low-cost consumer grade EEG hardware · Peterson et al. (2022)
50.7%49.2%–52.1%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.
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; BrainOmni, EEGMamba, ST-EEGFormer and SingLEM under MIT; CodeBrain and ZUNA under Apache-2.0; 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 →
ds003810 is recorded at 125 Hz; its windows reach the encoder upsampled to 256 Hz, so they carry nothing above 62.5 Hz.
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 →
Learned reject option minus calibrated confidence, same classifier (a secondary contrast)
Error among the 80% most certain, difference
+0.9 pp−0.4 pp–+2.4 ppNo difference resolved: the interval includes zero.
10
Limits: Ten people, two per test fold. Bootstrap intervals are crude; no person-level dispersion is published. Balanced, so no deployment rate. Its learned-reject contrasts are secondary. Calibration data are participant-disjoint inner out-of-fold scores (cross-fit). Inner models see about 3/4 of the training people, so they may be less confident than the full model whose scores are tested. This approximation is part of every calibrated figure. Rejecting likely errors on known classes is not detecting unfamiliar input. An uncertainty threshold may not flag an unseen class (https://arxiv.org/abs/2603.13324). Route 1 makes no out-of-distribution claim. A contrast is called a difference only when its paired 95% participant-bootstrap interval excludes 0 (no multiplicity correction); otherwise "no difference resolved". No ranking where intervals overlap. Descriptive entries are never called differences. Every limitation of route 1 →
Source and licence
ds003810
CreditPeterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/
The dataset authors ask you to cite: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 ↗ · as their record asks ↗
BCI Report does not redistribute any recording. These are aggregate measurements computed by BCI Report under the licence above; the data remain their creators’.
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