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_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) wrt. y.

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.

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.