Inverse solution
loose
module-attribute
¶
loose = 0.2
Value that weights the source variances of the dipole components
that are parallel (tangential) to the cortical surface. If 0
, then the
inverse solution is computed with fixed orientation.
If 1
, it corresponds to free orientation.
The default value, 'auto'
, is set to 0.2
for surface-oriented source
spaces, and to 1.0
for volumetric, discrete, or mixed source spaces,
unless fixed is True
in which case the value 0. is used.
depth
module-attribute
¶
depth = 0.8
If float (default 0.8), it acts as the depth weighting exponent (exp
)
to use (must be between 0 and 1). None is equivalent to 0, meaning no
depth weighting is performed. Can also be a dict
containing additional
keyword arguments to pass to :func:mne.forward.compute_depth_prior
(see docstring for details and defaults).
inverse_method
module-attribute
¶
inverse_method = 'dSPM'
Use minimum norm, dSPM (default), sLORETA, or eLORETA to calculate the inverse solution.
noise_cov
module-attribute
¶
noise_cov = (None, 0)
Specify how to estimate the noise covariance matrix, which is used in inverse modeling.
If a tuple, it takes the form (tmin, tmax)
with the time specified in
seconds. If the first value of the tuple is None
, the considered
period starts at the beginning of the epoch. If the second value of the
tuple is None
, the considered period ends at the end of the epoch.
The default, (None, 0)
, includes the entire period before the event,
which is typically the pre-stimulus period.
If 'emptyroom'
, the noise covariance matrix will be estimated from an
empty-room MEG recording. The empty-room recording will be automatically
selected based on recording date and time. This cannot be used with EEG data.
If 'rest'
, the noise covariance will be estimated from a resting-state
recording (i.e., a recording with task-rest
and without a run
in the
filename).
If 'ad-hoc'
, a diagonal ad-hoc noise covariance matrix will be used.
You can also pass a function that accepts a BIDSPath
and returns an
mne.Covariance
instance. The BIDSPath
will point to the file containing
the generated evoked data.
Example
Use the period from start of the epoch until 100 ms before the experimental event:
noise_cov = (None, -0.1)
Use the time period from the experimental event until the end of the epoch:
noise_cov = (0, None)
Use an empty-room recording:
noise_cov = 'emptyroom'
Use a resting-state recording:
noise_cov = 'rest'
Use an ad-hoc covariance:
noise_cov = 'ad-hoc'
Use a custom covariance derived from raw data:
def noise_cov(bids_path):
bp = bids_path.copy().update(task='rest', run=None, suffix='meg')
raw_rest = mne_bids.read_raw_bids(bp)
raw.crop(tmin=5, tmax=60)
cov = mne.compute_raw_covariance(raw, rank='info')
return cov
source_info_path_update
module-attribute
¶
source_info_path_update = dict(suffix='ave')
When computing the forward and inverse solutions, by default the pipeline
retrieves the mne.Info
object from the cleaned evoked data. However, in
certain situations you may wish to use a different Info
.
This parameter allows you to explicitly specify from which file to retrieve the
mne.Info
object. Use this parameter to supply a dictionary to
BIDSPath.update()
during the forward and inverse processing steps.
Example
Use the Info
object stored in the cleaned epochs:
source_info_path_update = {'processing': 'clean',
'suffix': 'epo'}
inverse_targets
module-attribute
¶
inverse_targets = ['evoked']
On which data to apply the inverse operator. Currently, the only supported
target is 'evoked'
. If no inverse computation should be done, pass an
empty list, []
.
Example
Compute the inverse solution on evoked data:
inverse_targets = ['evoked']
Don't compute an inverse solution:
inverse_targets = []