mne.compute_proj_epochs

mne.compute_proj_epochs(epochs, n_grad=2, n_mag=2, n_eeg=2, n_jobs=1, desc_prefix=None, meg='separate', verbose=None)

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.

Parameters

epochsinstance of Epochs

The epochs containing the artifact.

n_gradint

Number of vectors for gradiometers.

n_magint

Number of vectors for magnetometers.

n_eegint

Number of vectors for EEG channels.

n_jobsint

The number of jobs to run in parallel (default 1). If -1, it is set to the number of CPU cores. Requires the joblib package. 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.

megstr

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.

Returns

projs: list

List of projection vectors.

See also

compute_proj_raw, compute_proj_evoked

Examples using mne.compute_proj_epochs

Working with CTF data: the Brainstorm auditory dataset

Background on projectors and projections

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mne.Projection

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mne.compute_proj_evoked