pyriemann.clustering.Potato

class pyriemann.clustering.Potato(metric='riemann', threshold=3, n_iter_max=100, pos_label=1, neg_label=0)

Artefact detection with the Riemannian Potato.

The Riemannian Potato [1] is a clustering method used to detect artifact in EEG signals. The algorithm iteratively estimates the centroid of clean signal by rejecting every trial that is too far from it.

Parameters:

metricstring, default=’riemann’

The type of metric used for centroid and distance estimation.

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.

n_iter_maxint, default=100

The maximum number of iteration to reach convergence.

pos_labelint, default=1

The positive label corresponding to clean data.

neg_labelint, default=0

The negative label corresponding to artifact data.

See also

Kmeans

MDM

Notes

New in version 0.2.3.

References

[1]

The Riemannian Potato: an automatic and adaptive artifact detection method for online experiments using Riemannian geometry A. Barachant, A Andreev, and M. Congedo. TOBI Workshop lV, Jan 2013, Sion, Switzerland. pp.19-20.

[2]

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

Attributes:

covmean_ndarray, shape (n_channels, n_channels)

Centroid of potato.

__init__(metric='riemann', threshold=3, n_iter_max=100, pos_label=1, neg_label=0)

Init.

fit(X, y=None)

Fit the potato from covariance matrices.

Fit the potato from covariance matrices, with an iterative outlier removal to obtain a reliable potato.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

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:

selfPotato instance

The Potato 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 from covariance matrices.

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

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

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:

selfPotato instance

The Potato instance.

predict(X)

Predict artefact from data.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

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)

Return probability of belonging to the potato / being clean.

It is the probability to reject the null hypothesis “clean data”, computing the right-tailed probability from z-score.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

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 = '$UNCHANGED$') → Potato

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 = '$UNCHANGED$') → Potato

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-distance to the centroid (z-score).

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns:

zndarray, shape (n_matrices,)

the normalized log-distance to the centroid.