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.
Epochs
The epochs containing the artifact.
int
Number of vectors for gradiometers.
int
Number of vectors for magnetometers.
int
Number of vectors for EEG channels.
int
| 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.
str
| None
The description prefix to use. If None, one will be created based on the event_id, tmin, and tmax.
str
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.
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
List of projection vectors.
See also
mne.compute_proj_epochs
#Working with CTF data: the Brainstorm auditory dataset
Background on projectors and projections