eelbrain.pipeline.MneExperiment.load_evoked_stc¶
-
MneExperiment.
load_evoked_stc
(subjects=None, baseline=True, src_baseline=False, cat=None, keep_evoked=False, morph=False, mask=False, data_raw=False, vardef=None, samplingrate=None, decim=None, ndvar=True, **state)¶ Load evoked source estimates.
- Parameters
subjects (str | 1 | -1) – Subject(s) for which to load data. Can be a single subject name or a group name such as
'all'
.1
to use the current subject;-1
for the current group. Default is current subject (or group ifgroup
is specified).baseline (
Union
[bool
,Tuple
[Optional
[float
],Optional
[float
]]]) – Apply baseline correction using this period in sensor space. True to use the epoch’s baseline specification. The default is True.src_baseline (
Union
[bool
,Tuple
[Optional
[float
],Optional
[float
]]]) – Apply baseline correction using this period in source space. True to use the epoch’s baseline specification. The default is to not apply baseline correction.cat (
Optional
[Sequence
[Union
[str
,Tuple
[str
, …]]]]) – Only load data for these cells (cells of model).keep_evoked (
bool
) – Keep the sensor space data in the Dataset that is returned (default False).morph (
bool
) – Morph the source estimates to the common_brain (default False).mask (
Union
[bool
,str
]) – Discard data that is labelledunknown
by the parcellation. Parcellation name (str
) to specify a parcellation,True
to use the state-parc` state parameter. Only applies whenndvar=True
, defaultFalse
.data_raw (
bool
) – Keep themne.io.Raw
instance inds.info['raw']
(default False).vardef (
Optional
[str
]) – Name of a test defining additional variables.samplingrate (
Optional
[int
]) – Samplingrate in Hz for the analysis (default is specified in epoch definition).decim (
Optional
[int
]) – Data decimation factor (alternative tosamplingrate
).ndvar (
bool
) – Add the source estimates as NDVar named “src” instead of a list ofmne.SourceEstimate
objects named “stc” (default True)... –
Applicable State Parameters:
raw: preprocessing pipeline
epoch: which events to use and time window
rej (trial rejection): which trials to use
model: how to group trials into conditions
equalize_evoked_count: control number of trials per cell
cov: covariance matrix for inverse solution
src: source space
inv: inverse solution