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name: paradigms-and-pipelines description: "Choose a MOABB task paradigm and build a verified preprocessing, feature, classifier, filter-bank, or YAML pipeline for EEG decoding." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Paradigms and pipelines

Use this route when a task must turn EEG recordings or already-epoched arrays into sklearn-compatible inputs and predictions. Start by identifying the task family, class/event semantics, and whether the consumer expects a NumPy array, MNE Epochs, or a filter-bank tensor. Follow the shared installation and import policy in the MOABB root skill. Route dataset selection, downloads, subject/session discovery, and cache configuration to dataset-management. Route train/test or cross-subject protocol choices to evaluations-and-benchmarks.

Fast routing

Task evidence	Paradigm	Typical output and default score
Left/right or N-class imagined movement	LeftRightImagery or MotorImagery	3-D array (trials, channels, times); ROC-AUC for two classes, otherwise accuracy
Imagined words, phonemes, or speech	SpeechImagery	Same imagery machinery, broadband default 1–100 Hz; validate the dataset's event labels
Target versus non-target ERP	P300	3-D array or MNE Epochs; ROC-AUC
Frequency-tagged visual stimulation	SSVEP or FilterBankSSVEP	3-D or 4-D filter-bank data; accuracy except two-class ROC-AUC
Binary/multilevel coded visual stimulation	CVEP or FilterBankCVEP	3-D/4-D c-VEP epochs; accuracy except two-class ROC-AUC
No event markers; fixed windows	FixedIntervalWindowsProcessing	Windows labeled "Window"; specify resample when sample-count properties are needed

When the request is underspecified, ask whether the signal is an ERP response, frequency-tagged stimulation, or imagery before selecting a class. Do not infer P300, SSVEP, or motor imagery from an arbitrary binary label vector.

Minimal procedure

1. Confirm the dataset's paradigm, event_id, interval, channel names, and sampling rate. Use dataset-management for acquisition; this route only consumes a dataset object or a prepared array.
2. Choose event labels and class count. used_events(dataset) is the final check; an absent event or too-small class set is a configuration error, not a reason to silently use all events.
3. Set fmin/fmax or filters, tmin/tmax, optional baseline, channels, resample, overlap, and reject_by_annotation. tmax is relative to the dataset task interval; tmax <= tmin raises ValueError.
4. Call paradigm.get_data(dataset, subjects=[...]). The result is (X, labels, metadata). Default X is a 3-D NumPy array; use return_epochs=True for an MNE Epochs object or return_raws=True for raw objects. return_epochs and return_raws are mutually exclusive.
5. Fit the classifier inside a sklearn Pipeline and pass the pipeline to the evaluation route. Never fit a scaler, CSP, covariance, template, or classifier on all trials before a split.
6. Run the bundled deterministic checks: python scripts/smoke_xy_dataset.py --tiny-fixture and python scripts/smoke_preprocessing.py --tiny-fixture.

For exact constructor defaults and output contracts, use api-reference.md. For end-to-end choices use workflows.md; for YAML use pipeline-configs.md; for failures use troubleshooting.md.

Bundled offline helpers are smoke_xy_dataset.py and smoke_preprocessing.py. They are safe to run from any working directory and never acquire a dataset.

Configuration patterns

Paradigm preprocessing

from moabb.paradigms import LeftRightImagery

paradigm = LeftRightImagery(
    fmin=8, fmax=35, tmin=0.5, tmax=3.5,
    channels=["C3", "Cz", "C4"], resample=200.0,
)
X, y, meta = paradigm.get_data(dataset, subjects=[1])

Use FilterBankLeftRightImagery(filters=[...]) when the pipeline consumes a filter bank. A filter-bank paradigm returns (trials, channels, times, bands) for array output. FilterBank then applies a cloned estimator independently to each last-axis band and concatenates its 2-D features by default.

sklearn composition

For a 3-D array, LogVariance() produces a 2-D (trials, channels) feature matrix and can precede LDA or another sklearn classifier. CSP, covariance plus tangent-space, and MNE decoders are also pipeline steps. For a 4-D filter bank, use FilterBank(estimator=...); the inner estimator must return 2-D features. ExtendedSSVEPSignal() converts SSVEP filter-bank data to (trials, channels * bands, times) for covariance methods.

The SSVEP CCA-family (SSVEP_CCA, SSVEP_MsetCCA, SSVEP_itCCA, SSVEP_eCCA) consumes MNE Epochs when fitted directly. Preserve string frequency labels or pass an explicit freq_map when labels have been encoded as ordinal integers. TRCA, TRCA-R, SSCOR, and TDCA are filter-bank SSVEP classifiers; reduce n_fbands, n_delay, or estimator complexity for a tiny smoke test, not for an unreviewed scientific comparison.

YAML boundary

create_pipeline_from_config(config) accepts a list of component mappings, not a file path. Each mapping has name, from, and optional parameters. Load a trusted local YAML file with a safe YAML loader, validate its shape, and pass config["pipeline"]. Class imports are dynamic, so do not execute untrusted configuration. A config's paradigms and name metadata are used by benchmark tooling; they do not alter the sklearn pipeline itself.

Boundaries and safety

• Real datasets can require network access, provider credentials, large disk, and long preprocessing. The bundled scripts never download data.
• mne, scikit-learn, and NumPy are core runtime assumptions. pyriemann, scipy, and YAML support are needed by selected pipelines; deep-learning pipelines are outside this route's selected core and are not restored by importing MOABB.
• Keep channel order explicit when comparing datasets. Use an intersection of channels or a deliberate interpolation policy; do not silently accept an empty or reordered channel selection.
• Inspect X.ndim, X.shape, type(X), len(y), and meta.shape before fitting. A 3-D epoch array is not a 2-D feature matrix until a transformer makes that conversion.
• P300 uses its event relabeling behavior unless ignore_relabelling=True; preserve the target/non-target semantics when interpreting ROC-AUC.
• SSVEP frequency inference requires physical frequencies. Encoded labels such as 0, 1, 2 can be ambiguous; use freq_map={0: 13.0, 1: 17.0, 2: 21.0} when necessary.

Verification handoff

The bundled scripts are safe adapted replacements for the X/y and preprocessing recipes and are the preferred offline checks for this runtime graph. Separate repository verification may add focused CPU tests, but those tests are not a runtime dependency of this skill. See the references for expected observations and recovery actions; do not treat a successful import alone as evidence that a paradigm matches a dataset.