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 high-pass filter

Low-pass filtering improves specificity.

High-pass filtering should be kept small, to keep some sensitivity.

Filtering is only meaningful on evenly-sampled signals.


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 one-line 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 non-finite values (NANs and infs) found in the data will be replaced by zeros.


cleaned_signals: numpy.ndarray

Input signals, cleaned. Same shape as signals.


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): 189-210.