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

Compute SSP (signal-space projection) vectors on epoched 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.

epochsinstance of Epochs

The epochs containing the artifact.


Number of vectors for gradiometers.


Number of vectors for magnetometers.


Number of vectors for EEG channels.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_backend() context manager that sets another value for n_jobs. Number of jobs to use to compute covariance.

desc_prefixstr | None

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


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 version 0.18.

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.

projs: list

List of projection vectors.

Examples using mne.compute_proj_epochs#

Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset

Repairing artifacts with SSP

Repairing artifacts with SSP