EEGNet: results on public EEG datasets

ds003810 · Core matrix · motor imagery & rest

Source page: Core-matrix protocol: Motor imagery & rest · Chance level 50.0% · experiments.json

ds003810 · Reliable decisions · when to decline a decision (crude: ten people)

Source page: When not to act · reliable-decisions-update.json

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 →

EEGMAT · Core matrix · arithmetic & rest

Source page: Core-matrix protocol: Arithmetic & rest · Chance level 50.0% · experiments.json

EEGMAT · Reliable decisions · when to decline a decision

Source page: When not to act · reliable-decisions-update.json

Limits: Balanced by design. Error rates describe a 1:1 mix and are a method comparison, not a deployment rate. Class equals recording, so recording-level differences are part of what any model or selector can use. 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 →

BETA · Core matrix · SSVEP, 8 channels

Source page: Core-matrix protocol: SSVEP · 8 channels · Chance level 2.5% · experiments.json

BETA · Core matrix · SSVEP, 4 channels

Source page: Core-matrix protocol: SSVEP · 4 channels · Chance level 2.5% · experiments.json

BETA · Reliable decisions · when to decline a decision

Source page: When not to act · reliable-decisions-update.json

Limits: Uniform target prior by design; cue-paced lab spelling with no idle state, so no false-activation figure of any kind. 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 →

ds006593 · Core matrix · P300 target ERP

Source page: Core-matrix protocol: P300 target ERP · Chance level 50.0% · experiments.json

TMNRED / ds005383 · Core matrix · semantic target ERP

Source page: Core-matrix protocol: Semantic target ERP · Chance level 50.0% · experiments.json

EESM19 scalp subset · Core matrix · sleep staging

Source page: Core-matrix protocol: Sleep staging · Chance level 20.0% · experiments.json

EESM19 scalp subset · One model, several questions · sleep, the full EESM19 release (crude: 20 people, one seed)

Source page: One model, several questions · shared-representation-update.json

Limits: The questions are label-backed classification targets from one dataset each, asked by identifier. They are not language prompts and say nothing about unseen questions (route 3). EESM19 full (S13) uses every stored epoch that passes the published core rules at its natural stage mix; it is a different protocol from the site's balanced sleep-scalp subset. SL-E and SL-F are largely predictable from the current stage; no stage-only read-out was computed on EESM19. A contrast shows a difference when its paired 95% interval excludes zero; it is equivalent when the whole interval lies within ±2 pp, the margin fixed before any result, and an arm is non-inferior when the interval rules out its being 2 pp or more behind. No multiplicity correction: across route 2’s 21 primary comparisons and its secondary ones, about one in 20 comparisons with no true difference may show one by chance. Every limitation of route 2 →

ds005342 · Core matrix · idle & command

Source page: Core-matrix protocol: Idle & command · experiments.json

Wearable SSVEP BCI dataset (dry and wet electrodes) · Dry vs. wet sensor transfer

Source page: Dry vs. wet electrodes · deployment-topics.json

OpenBMI motor imagery (Lee et al. 2019) · One model, several questions · motor imagery (route 2)

Source page: One model, several questions · Chance level 50.0% · shared-representation-update.json

Limits: The questions are label-backed classification targets from one dataset each, asked by identifier. They are not language prompts and say nothing about unseen questions (route 3). Per-person balanced accuracy averages people equally; it compares arms and is not a deployment error rate. The OpenBMI numbers here use route 2's own 2-s windows, label-free high-pass and person-disjoint folds; they are not comparable with the site's OpenBMI cross-session calibration figures. A contrast shows a difference when its paired 95% interval excludes zero; it is equivalent when the whole interval lies within ±2 pp, the margin fixed before any result, and an arm is non-inferior when the interval rules out its being 2 pp or more behind. No multiplicity correction: across route 2’s 21 primary comparisons and its secondary ones, about one in 20 comparisons with no true difference may show one by chance. Every limitation of route 2 →

BOAS · Bitbrain Open Access Sleep dataset · One model, several questions · sleep (route 2)

Source page: One model, several questions · shared-representation-update.json

Limits: The questions are label-backed classification targets from one dataset each, asked by identifier. They are not language prompts and say nothing about unseen questions (route 3). Per-person balanced accuracy averages people equally; it compares arms and is not a deployment error rate. SL-E and SL-F are largely predictable from the current stage: read off the true current stage alone, they reach 61.9% and 59.2% balanced accuracy (100 people; descriptive). A contrast shows a difference when its paired 95% interval excludes zero; it is equivalent when the whole interval lies within ±2 pp, the margin fixed before any result, and an arm is non-inferior when the interval rules out its being 2 pp or more behind. No multiplicity correction: across route 2’s 21 primary comparisons and its secondary ones, about one in 20 comparisons with no true difference may show one by chance. Every limitation of route 2 →