pyriemann.channelselection.ElectrodeSelection

class pyriemann.channelselection.ElectrodeSelection(nelec=16, metric='riemann', n_jobs=1)

Channel selection based on a Riemannian geometry criterion.

For each class, a centroid is estimated, and the channel selection is based on the maximization of the distance between centroids. This is done by a backward elimination where the electrode that carries the less distance is removed from the subset at each iteration [1].

Parameters:
nelecint, default=16

The number of electrode to keep in the final subset.

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_jobsint, default=1

The number of jobs to use for the computation. This works by computing each of the class centroid in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.

Attributes:
covmeans_ndarray, shape (n_classes, n_channels, n_channels)

Centroids for each class.

dist_list

Distance at each iteration.

self.subelec_list

Indices of selected channels.

See also

Kmeans
FgMDM

References

[1]

Channel selection procedure using riemannian distance for BCI applications A. Barachant and S. Bonnet. The 5th International IEEE EMBS Conference on Neural Engineering, Apr 2011, Cancun, Mexico.

__init__(nelec=16, metric='riemann', n_jobs=1)

Init.

fit(X, y=None, sample_weight=None)

Find the optimal subset of electrodes.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

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

Labels for each matrix.

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

Weights for each matrix. If None, it uses equal weights.

Returns:
selfElectrodeSelection instance

The ElectrodeSelection instance.

fit_transform(X, y=None, sample_weight=None)

Fit and transform in a single function.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

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

Labels for each matrix.

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

Weights for each matrix. If None, it uses equal weights.

Returns:
X_newndarray, shape (n_matrices, n_elec, n_elec)

Set of SPD matrices after reduction of the number of channels.

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.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → ElectrodeSelection

Request metadata passed to the 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 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 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.

Added 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 fit.

Returns:
selfobject

The updated object.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

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.

transform(X)

Return reduced matrices.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns:
X_newndarray, shape (n_matrices, n_elec, n_elec)

Set of SPD matrices after reduction of the number of channels.