mne.decoding.Scaler

class mne.decoding.Scaler(info=None, scalings=None, with_mean=True, with_std=True)

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

infoinstance of Info | None

The measurement info. 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

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

__hash__(self, /)

Return hash(self).

fit(self, epochs_data, y=None)

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.

fit_transform(self, epochs_data, y=None)

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.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deepbool, optional

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

Returns

paramsmapping of str to any

Parameter names mapped to their values.

inverse_transform(self, epochs_data)

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.

set_params(self, **params)

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. Returns ——- self

transform(self, epochs_data)

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 mne.decoding.Scaler

Decoding (MVPA)