pyriemann.clustering.PotatoField

class pyriemann.clustering.PotatoField(n_potatoes=1, p_threshold=0.01, z_threshold=3, metric='riemann', n_iter_max=10, pos_label=1, neg_label=0)

Artefact detection with the Riemannian Potato Field.

The Riemannian Potato Field [1] is a clustering method used to detect artifact in EEG signals. The algorithm combines several potatoes of low dimension, each one being designed to capture specific artifact typically affecting specific subsets of channels and/or specific frequency bands.

Parameters:

n_potatoesint, default=1

Number of potatoes in the field.

p_thresholdfloat, default=0.01

Threshold on probability to being clean, in (0, 1), combining probabilities of potatoes using Fisher’s method.

z_thresholdfloat, default=3

Threshold on z-score of distance to reject artifacts. It is the number of standard deviations from the mean of distances to the centroid.

metricstring | dict, default=”riemann”

Metric used for mean estimation (for the list of supported metrics, see pyriemann.utils.mean.mean_covariance()) and for distance estimation (see pyriemann.utils.distance.distance()). The metric can be a dict with two keys, “mean” and “distance” in order to pass different metrics.

n_iter_maxint, default=10

The maximum number of iteration to reach convergence.

pos_label: int, default=1

The positive label corresponding to clean data.

neg_label: int, default=0

The negative label corresponding to artifact data.

See also

Potato

Notes

New in version 0.3.

References

[1]

The Riemannian Potato Field: A Tool for Online Signal Quality Index of EEG Q. Barthélemy, L. Mayaud, D. Ojeda, and M. Congedo. IEEE Transactions on Neural Systems and Rehabilitation Engineering, IEEE Institute of Electrical and Electronics Engineers, 2019, 27 (2), pp.244-255

__init__(n_potatoes=1, p_threshold=0.01, z_threshold=3, metric='riemann', n_iter_max=10, pos_label=1, neg_label=0)

Init.

fit(X, y=None)

Fit the potato field from covariance matrices.

Fit the potato field from covariance matrices, with iterative outlier removal to obtain reliable potatoes.

Parameters:

Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD matrices, each corresponding to a different subset of EEG channels and/or filtering with a specific frequency band.

yNone | ndarray, shape (n_matrices,), default=None

Labels corresponding to each matrix: positive (resp. negative) label corresponds to a clean (resp. artifact) matrix. If None, all matrices are considered as clean.

Returns:

selfPotatoField instance

The PotatoField instance.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

partial_fit(X, y=None, alpha=0.1)

Partially fit the potato field from covariance matrices.

This partial fit can be used to update dynamic or semi-dymanic online potatoes with clean EEG.

Parameters:

Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD matrices, each corresponding to a different subset of EEG channels and/or filtering with a specific frequency band.

yNone | ndarray, shape (n_matrices,), default=None

Labels corresponding to each matrix: positive (resp. negative) label corresponds to a clean (resp. artifact) matrix. If None, all matrices are considered as clean.

alphafloat, default=0.1

Update rate in [0, 1] for the centroid, and mean and standard deviation of log-distances: 0 for no update, 1 for full update.

Returns:

selfPotatoField instance

The PotatoField instance.

predict(X)

Predict artefact from data.

Parameters:

Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD matrices, each corresponding to a different subset of EEG channels and/or filtering with a specific frequency band.

Returns:

predndarray of bool, shape (n_matrices,)

The artefact detection: True if the matrix is clean, and False if the matrix contain an artefact.

predict_proba(X)

Predict probability obtained combining probabilities of potatoes.

Predict probability obtained combining probabilities of potatoes using Fisher’s method. A threshold of 0.01 can be used.

Parameters:

Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD matrices, each corresponding to a different subset of EEG channels and/or filtering with a specific frequency band.

Returns:

probandarray, shape (n_matrices,)

Matrix is considered as normal/clean for high value of proba. It is considered as abnormal/artifacted for low value of proba.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

set_output(*, transform=None)

Set output container.

See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

• “default”: Default output format of a transformer

• “pandas”: DataFrame output

• None: Transform configuration is unchanged

Returns:

selfestimator instance

Estimator instance.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_partial_fit_request(*, alpha: bool | None | str = '$UNCHANGED$') → PotatoField

Request metadata passed to the partial_fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to partial_fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to partial_fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

alphastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for alpha parameter in partial_fit.

Returns:

selfobject

The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → PotatoField

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.

transform(X)

Return the normalized log-distances to the centroids.

Return the normalized log-distances to the centroids, ie geometric z-scores of distances.

Parameters:

Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD matrices, each corresponding to a different subset of EEG channels and/or filtering with a specific frequency band.

Returns:

zndarray, shape (n_matrices, n_potatoes)

The normalized log-distances to the centroids.