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.glm.first_level.compute_regressor(exp_condition, hrf_model, frame_times, con_id='cond', oversampling=50, fir_delays=None, min_onset=-24)[source]

Convolve regressors with HRF model.

exp_conditionarray-like of shape (3, n_events)

yields description of events for this condition as a (onsets, durations, amplitudes) triplet

hrf_modelstr, function, list of functions, or None

This parameter defines the HRF model to be used. It can be a string if you are passing the name of a model implemented in Nilearn. Valid names are:

It can also be a custom model. In this case, a function should be provided for each regressor. Each function should behave as the other models implemented within Nilearn. That is, it should take both t_r and oversampling as inputs and return a sample numpy array of appropriate shape.


It is expected that “spm” standard and “glover” models would not yield large differences in most cases.


In case of “glover” and “spm” models, the derived regressors are orthogonalized with respect to the main one.

frame_timesarray of shape (n_scans)

the desired sampling times

con_idstring, default=’cond’

Identifier of the condition

oversamplingint, default=50

Oversampling factor to perform the convolution.

fir_delays[int] 1D-array-like, optional

Delays (in scans) used in case of a finite impulse response model.

min_onsetfloat, default=-24

Minimal onset relative to frame_times[0] (in seconds) events that start before frame_times[0] + min_onset are not considered.

computed_regressorsarray of shape(n_scans, n_reg)

Computed regressors sampled at frame times.

reg_nameslist of strings

Corresponding regressor names.

Examples using nilearn.glm.first_level.compute_regressor

Example of MRI response functions

Example of MRI response functions