Everything that produced the scores below — cohort, split, electrodes, window, what each method was allowed to learn, and what guessing would score — as the released protocol file states it. Compare scores within this protocol only.
Balanced accuracy (primary) · Macro F1 (secondary)
Results
Every method run under this protocol, with the score the released results file holds. Compare down this table only: other protocols differ in cohort, electrodes, window or chance level.
Spectral ridge
54.9% (53.2%–56.7%)
CSP+LDA
47.1% (43.8%–50.2%)
LaBraM
53.5% (51.4%–55.4%)
CBraMod
62.9% (59.6%–66.5%)
EEGNet
71.0% (65.0%–76.3%)
0%25%50%75%100%
Balanced accuracy, every method under this protocol. Dot: the estimate; line: descriptive 95% interval; dashed line: chance level.
71.0%65.0%–76.3%Single seed — the highest of the seeds run (see Stability)
0.680Mean across held-out participants
15
10
55.5 s
Scoring time includes fitting and prediction, may include accelerator waiting, and excludes data preparation. It is configuration-specific, not a hardware benchmark.
Not run under this protocol:ShallowFBCSPNet, Deep4Net, Standard CCA, Temporal ridge. A method missing here was not run on this protocol — that is not a failure.
Read with care
Ten-person laboratory task with prompted rest. This is not continuous-idle monitoring or a physical low-channel headset test.
The protocol, step by step
5 participant-disjoint folds. All recordings from a person stay together. Each person contributes to the held-out predictions once.
epoching: two seconds from the class annotation onset; amplitude: converted to microvolts according to source calibration, then per-channel epoch mean removed; resampling: none; native sampling rate retained; selection: natural file/event order; when over the cap, retain 60 evenly spaced event indices per participant/class; source_units: Microv declared by BIDS channels.tsv; EDF physical dimension is absent, so MNE returns the source numeric microvolt values without SI scaling
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).
run 0 is real dominant-hand movement and is excluded; only imagery runs 1-4 contribute
the source reports online 0.5-45 Hz filtering
controlled cue-locked laboratory windows; this does not measure continuous false activations
foundation-model pretraining overlap is unknown
EEGNet three-seed mean 69.47%; sample SD 1.43 percentage points; range 68.17–71.00%. Main table retains the original fixed seed; this is not a confidence interval.
Stability
EEGNet three-seed mean 69.47%; sample SD 1.43 percentage points; range 68.17–71.00%. Main table retains the original fixed seed; this is not a confidence interval.
Seeds run for EEGNet: 71.00% · 69.25% · 68.17%; mean 69.47%
Same participants, folds, preprocessing and 20-epoch budget. Three seeds measure initialization variability, not population uncertainty. Main table retains its preselected seed; no best-seed selection.
Pretraining exposure
Unknown unless explicitly documented; no unseen-pretraining claim.
Notes on the methods
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.
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. EEGNet three-seed mean 69.47%; sample SD 1.43 percentage points; range 68.17–71.00%. Main table retains the original fixed seed; this is not a confidence interval.
Model terms
Spectral ridge: Trained from scratch / deterministic reference; no third-party pretrained weights. Braindecode BSD-3-Clause; MNE/scikit-learn BSD where used.
CSP+LDA: 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.
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 belong to the people credited.
Permitted scopePersonal noncommercial research; aggregate results only
Correction on record · : The ds003810 credit links only the 2022 Data in Brief description. The OpenNeuro record asks users to cite Peterson, Galván, Hernández and Spies, Heliyon 6(3):e03425 (2020); the dataset page now gives both. Corrections register →
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