mne.compute_proj_evoked(evoked, n_grad=2, n_mag=2, n_eeg=2, desc_prefix=None, meg='separate', verbose=None)[source]#

Compute SSP (signal-space projection) vectors on evoked data.

This function aims to find those SSP vectors that will project out the n most prominent signals from the data for each specified sensor type. Consequently, if the provided input data contains high levels of noise, the produced SSP vectors can then be used to eliminate that noise from the data.

evokedinstance of Evoked

The Evoked obtained by averaging the artifact.


Number of vectors for gradiometers.


Number of vectors for magnetometers.


Number of vectors for EEG channels.

desc_prefixstr | None

The description prefix to use. If None, one will be created based on tmin and tmax.

New in v0.17.


Can be ‘separate’ (default) or ‘combined’ to compute projectors for magnetometers and gradiometers separately or jointly. If ‘combined’, n_mag == n_grad is required and the number of projectors computed for MEG will be n_mag.

New in v0.18.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.


List of projection vectors.

Examples using mne.compute_proj_evoked#

Brainstorm raw (median nerve) dataset

Brainstorm raw (median nerve) dataset