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:

info : instance of Info | None

The measurement info. Only necessary if scalings is a dict or None.

scalings : dict, string, defaults to None.

Scaling method to be applied to data channel wise.

with_mean : boolean, True by default

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

with_std : boolean, True by default

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__() <==> hash(x)
fit(epochs_data, y) Standardize data across channels.
fit_transform(X[, 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[, y]) Standardize data across channels.
__hash__() <==> hash(x)
fit(epochs_data, y)[source]

Standardize data across channels.

Parameters:

epochs_data : array, shape (n_epochs, n_channels, n_times)

The data to concatenate channels.

y : array, shape (n_epochs,)

The label for each epoch.
Returns:

self : instance of Scaler

Returns the modified instance.
fit_transform(X, y=None, **fit_params)[source]

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:

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.
Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:

deep : boolean, optional

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

params : mapping of string to any

Parameter names mapped to their values.
inverse_transform(epochs_data)[source]

Invert standardization of data across channels.

Parameters:

epochs_data : array, shape (n_epochs, n_channels, n_times)

The data.
Returns:

X : array, 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(**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. Returns ——- self

transform(epochs_data, y=None)[source]

Standardize data across channels.

Parameters:

epochs_data : array, shape (n_epochs, n_channels, n_times)

The data.

y : None | array, shape (n_epochs,)

The label for each epoch. If None not used. Defaults to None.
Returns:

X : array, 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.