version: "1.0.0" name: bci-results-reporting description: "Write BCI and EEG machine-learning results for papers. Use for subject-wise performance tables, accuracy, balanced accuracy, AUC, F1, confusion matrix, ITR, confidence intervals, permutation tests, baseline comparison, leakage disclosure, model architecture table, and BCI manuscript reporting."

BCI Results Reporting

Use this skill to turn EEG/BCI modeling outputs into manuscript-ready results.

Inputs

• Model outputs and metrics.
• Split design and leakage audit.
• Baselines and comparison models.
• Target journal or conference style.

Workflow

1. Report the data split first, because it defines the claim.
2. Provide subject-wise and aggregate metrics.
3. Include baseline and chance-level comparison.
4. Report uncertainty: confidence intervals, standard deviation across subjects, or paired tests.
5. Include confusion matrix and class-level metrics for imbalanced tasks.
6. Disclose preprocessing, feature extraction, tuning, and leakage safeguards.
7. State deployment limits: within-subject, cross-session, cross-subject, offline, pseudo-online, or online.

Hard Constraints

• Do not report only the best fold or best seed.
• Do not omit failed subjects.
• Do not describe offline results as real-time BCI.
• Do not hide leakage risks or post hoc model selection.

Output

Results table plan:
Subject-wise table:
Model-comparison table:
Figure plan:
Leakage and split disclosure:
Results paragraph:
Limitations paragraph: