Compute SSP (signal-space projection) vectors on continuous 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.
A raw object to use the data from.
Time (in sec) to start computing SSP.
Time (in sec) to stop computing SSP. None will go to the end of the file.
Duration (in sec) to chunk data into for SSP If duration is None, data will not be chunked.
Number of vectors for gradiometers.
Number of vectors for magnetometers.
Number of vectors for EEG channels.
Epoch rejection configuration (see Epochs).
Epoch flat configuration (see Epochs).
The number of jobs to run in parallel. If
-1, it is set
to the number of CPU cores. Requires the
None (default) is a marker for ‘unset’ that will be interpreted
n_jobs=1 (sequential execution) unless the call is performed under
joblib.parallel_backend() context manager that sets another
Number of jobs to use to compute covariance.
Can be ‘separate’ (default) or ‘combined’ to compute projectors
for magnetometers and gradiometers separately or jointly.
n_mag == n_grad is required and the number of
projectors computed for MEG will be
New in version 0.18.
List of projection vectors.