pyriemann.channelselection.FlatChannelRemover

class pyriemann.channelselection.FlatChannelRemover

Finds and removes flat channels.

Attributes
channels_ndarray, shape (n_good_channels)

The indices of the non-flat channels.

__init__(*args, **kwargs)
fit(X, y=None)

Find flat channels.

Parameters
Xndarray, shape (n_matrices, n_channels, n_times)

Multi-channel time-series.

yNone

Not used, here for compatibility with sklearn API.

Returns
Xndarray, shape (n_matrices, n_good_channels, n_times)

Multi-channel time-series without flat channels.

fit_transform(X, y=None)

Find and remove flat channels.

Parameters
Xndarray, shape (n_matrices, n_channels, n_times)

Multi-channel time-series.

yNone

Not used, here for compatibility with sklearn API.

Returns
Xndarray, shape (n_matrices, n_good_channels, n_times)

Multi-channel time-series without flat channels.

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_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)

Remove flat channels.

Parameters
Xndarray, shape (n_matrices, n_channels, n_times)

Multi-channel time-series.

Returns
Xndarray, shape (n_matrices, n_good_channels, n_times)

Multi-channel time-series without flat channels.