coco_pipe.decoding.scalers

Classes

SubjectStandardScaler

Standardization Strategy:

Module Contents

class coco_pipe.decoding.scalers.SubjectStandardScaler

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

Standardization Strategy: 1. Global standardization across all samples (StandardScaler). 2. Per-Subject centering (Mean subtraction within each subject).

global_scaler

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

transform(X, groups=None)

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

Fit the scaler to the data and then transform it.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Input data.

• y (array-like of shape (n_samples,), default=None) – Target values (ignored).

• groups (array-like of shape (n_samples,), default=None) – Group labels for per-subject centering.

Returns:

X_scaled – Scaled and centered data.

Return type:

array-like of shape (n_samples, n_features)