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name: timefreq-stats-decoding-simulation description: "Guide MNE-Python time-frequency, spectral, statistics, decoding, and simulation workflows for Raw, Epochs, Evoked, and source data." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

MNE-Python time-frequency, statistics, decoding, and simulation router

Use this sub-skill when a task needs spectra, time-frequency transforms, cross-spectral density, statistical inference, machine-learning/decoding, or synthetic MNE data.

Load order

1. For PSD/TFR/CSD, statistics, decoding, and simulation recipes, read references/workflows.md.
2. For verified signatures, object shapes, and API defaults, read references/api-reference.md.
3. For permutation clusters, adjacency, regression, cross-validation, CSP, and scikit-learn-dependent decoding details, read references/statistics-and-decoding.md.
4. If the task reports shape, baseline, frequency, permutation, adjacency, scikit-learn, or simulation errors, read references/troubleshooting.md.
5. For a deterministic local smoke check, run or adapt scripts/analysis_smoke.py.

Own these requests

• Power spectra and spectral estimates: Raw.compute_psd, Epochs.compute_psd, Spectrum, Welch/multitaper array helpers, frequency-band summaries.
• Time-frequency representations: Morlet/wavelet, multitaper, Stockwell, TFR objects, baseline correction, decimation, and output type choices.
• Cross-spectral density and frequency-domain beamformer inputs.
• Sensor/source statistics: permutation tests, cluster tests, adjacency, multiple-comparison correction, regression, and FDR.
• Decoding and machine learning: CSP, SSD, Xdawn, EMS, receptive fields, SlidingEstimator, GeneralizingEstimator, scikit-learn pipelines, scoring, leakage-safe cross-validation.
• Simulation: synthetic Raw, Evoked, SourceEstimate, source simulators, metrics, and generated data for tests/examples.

Route elsewhere

• Reading files, channel metadata ingestion, and synthetic RawArray basics belong to io-raw-data.
• Filtering, artifact correction, event extraction, epochs, evoked responses, covariance preprocessing, and rank preparation belong to preprocessing-epochs-evoked.
• Plotting spectra/TFR/topomaps/results belongs to visualization-reporting after this sub-skill chooses the analysis output.
• Source spaces, forward models, inverse operators, and beamformer filter construction belong to source-modeling-inverse; this sub-skill owns CSD and statistical analysis around those outputs.
• Installation/extras and no-download dataset checks belong to cli-datasets-config.

Operating rules

• Decide and document data shape before selecting an API. Sensor/epoch arrays, Epochs, Evoked, SourceEstimate, and NumPy arrays have different axis conventions and adjacency needs.
• Choose frequency parameters from sampling rate and epoch length; avoid asking for frequencies that cannot be resolved from the available data.
• Keep statistics independent of visualization: first compute arrays/statistics, then route plotting to the visualization sub-skill.
• Treat scikit-learn as an optional dependency for many decoding workflows. If unavailable, either install the documented extra/path or restrict the task to non-decoding analysis.
• Prevent data leakage. Fitting filters, scalers, CSP, feature selection, or decoders must happen inside cross-validation pipelines when estimating generalization.
• Never depend on the original repository checkout at runtime; use these references and bundled helpers.