Note
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.
7.11.1. nilearn.signal.clean¶

nilearn.signal.
clean
(signals, sessions=None, detrend=True, standardize=True, confounds=None, low_pass=None, high_pass=None, t_r=2.5, ensure_finite=False)¶ Improve SNR on masked fMRI signals.
This function can do several things on the input signals, in the following order:
 detrend
 standardize
 remove confounds
 low and highpass filter
Lowpass filtering improves specificity.
Highpass filtering should be kept small, to keep some sensitivity.
Filtering is only meaningful on evenlysampled signals.
Parameters: signals: numpy.ndarray
Timeseries. Must have shape (instant number, features number). This array is not modified.
sessions : numpy array, optional
Add a session level to the cleaning process. Each session will be cleaned independently. Must be a 1D array of n_samples elements.
confounds: numpy.ndarray, str or list of
Confounds timeseries. Shape must be (instant number, confound number), or just (instant number,) The number of time instants in signals and confounds must be identical (i.e. signals.shape[0] == confounds.shape[0]). If a string is provided, it is assumed to be the name of a csv file containing signals as columns, with an optional oneline header. If a list is provided, all confounds are removed from the input signal, as if all were in the same array.
t_r: float
Repetition time, in second (sampling period).
low_pass, high_pass: float
Respectively low and high cutoff frequencies, in Hertz.
detrend: bool
If detrending should be applied on timeseries (before confound removal)
standardize: bool
If True, returned signals are set to unit variance.
ensure_finite: bool
If True, the nonfinite values (NANs and infs) found in the data will be replaced by zeros.
Returns: cleaned_signals: numpy.ndarray
Input signals, cleaned. Same shape as signals.
Notes
Confounds removal is based on a projection on the orthogonal of the signal space. See Friston, K. J., A. P. Holmes, K. J. Worsley, J.P. Poline, C. D. Frith, et R. S. J. Frackowiak. “Statistical Parametric Maps in Functional Imaging: A General Linear Approach”. Human Brain Mapping 2, no 4 (1994): 189210.