mne.decoding.Scaler#

class mne.decoding.Scaler(info=None, scalings=None, with_mean=True, with_std=True)[source]#

Standardize channel data.

This class scales data for each channel. It differs from scikit-learn classes (e.g., sklearn.preprocessing.StandardScaler) in that it scales each channel by estimating μ and σ using data from all time points and epochs, as opposed to standardizing each feature (i.e., each time point for each channel) by estimating using μ and σ using data from all epochs.

Parameters:

infomne.Info | None

The mne.Info object with information about the sensors and methods of measurement. Only necessary if scalings is a dict or None.

scalingsdict, str, default None

Scaling method to be applied to data channel wise.

• if scalings is None (default), scales mag by 1e15, grad by 1e13, and eeg by 1e6.

• if scalings is dict, keys are channel types and values are scale factors.

• if scalings=='median', sklearn.preprocessing.RobustScaler is used (requires sklearn version 0.17+).

• if scalings=='mean', sklearn.preprocessing.StandardScaler is used.

with_meanbool, default True

If True, center the data using mean (or median) before scaling. Ignored for channel-type scaling.

with_stdbool, default True

If True, scale the data to unit variance (scalings='mean'), quantile range (scalings='median), or using channel type if scalings is a dict or None).

Methods

fit(epochs_data[, y])	Standardize data across channels.
fit_transform(epochs_data[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
inverse_transform(epochs_data)	Invert standardization of data across channels.
set_params(**params)	Set the parameters of this estimator.
transform(epochs_data)	Standardize data across channels.

fit(epochs_data, y=None)[source]#

Standardize data across channels.

Parameters:

epochs_dataarray, shape (n_epochs, n_channels, n_times)

The data to concatenate channels.

yarray, shape (n_epochs,)

The label for each epoch.

Returns:

selfinstance of Scaler

The modified instance.

Examples using fit:

Decoding (MVPA)

Decoding (MVPA)

fit_transform(epochs_data, y=None)[source]#

Fit to data, then transform it.

Fits transformer to epochs_data and y and returns a transformed version of epochs_data.

Parameters:

epochs_dataarray, shape (n_epochs, n_channels, n_times)

The data.

yNone | array, shape (n_epochs,)

The label for each epoch. Defaults to None.

Returns:

Xarray, shape (n_epochs, n_channels, n_times)

The data concatenated over channels.

Notes

This function makes a copy of the data before the operations and the memory usage may be large with big data.

Examples using fit_transform:

Decoding (MVPA)

Decoding (MVPA)

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, optional

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

Returns:

paramsdict

Parameter names mapped to their values.

inverse_transform(epochs_data)[source]#

Invert standardization of data across channels.

Parameters:

epochs_dataarray, shape (n_epochs, n_channels, n_times)

The data.

Returns:

Xarray, shape (n_epochs, n_channels, n_times)

The data concatenated over channels.

Notes

This function makes a copy of the data before the operations and the memory usage may be large with big data.

Examples using inverse_transform:

Decoding (MVPA)

Decoding (MVPA)

set_params(**params)[source]#

Set the parameters of this estimator.

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

Parameters:

**paramsdict

Parameters.

Returns:

instinstance

The object.

transform(epochs_data)[source]#

Standardize data across channels.

Parameters:

epochs_dataarray, shape (n_epochs, n_channels[, n_times])

The data.

Returns:

Xarray, shape (n_epochs, n_channels, n_times)

The data concatenated over channels.

Notes

This function makes a copy of the data before the operations and the memory usage may be large with big data.

Examples using transform:

Decoding (MVPA)

Decoding (MVPA)

Examples using mne.decoding.Scaler#

Decoding (MVPA)

Decoding (MVPA)