This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.
- nilearn.signal.high_variance_confounds(series, n_confounds=5, percentile=2.0, detrend=True)#
Return confounds time series extracted from series with highest variance.
Timeseries. A timeseries is a column in the “series” array. shape (sample number, feature number)
Number of confounds to return. Default=5.
Highest-variance series percentile to keep before computing the singular value decomposition, 0. <= percentile <= 100.
series.shape * percentile / 100must be greater than
Whether to detrend signals or not. Default=True.
Highest variance confounds. Shape: (samples, n_confounds)
This method is related to what has been published in the literature as ‘CompCor’ .
The implemented algorithm does the following:
compute sum of squares for each time series (no mean removal)
keep a given percentile of series with highest variances (percentile)
compute an svd of the extracted series
return a given number (n_confounds) of series from the svd with highest singular values.