eelbrain.pipeline.MneExperiment.load_epochs_stc

MneExperiment.load_epochs_stc(self, subjects=None, baseline=True, src_baseline=False, cat=None, keep_epochs=False, morph=False, mask=False, data_raw=False, vardef=None, decim=None, pad=0, ndvar=True, reject=True, **state)

Load a Dataset with stcs for single epochs

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 if group is specified). Warning: loading single trial data for multiple subjects at once uses a lot of memory, which can lead to a periodically unresponsive terminal).

baseline : bool | tuple

Apply baseline correction using this period in sensor space. True to use the epoch’s baseline specification (default).

src_baseline : bool | tuple

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 : sequence of cell-names

Only load data for these cells (cells of model).

keep_epochs : bool | ‘ndvar’ | ‘both’

Keep the sensor space data in the Dataset that is returned (default False; True to keep mne.Epochs object; 'ndvar' to keep NDVar; 'both' to keep both).

morph : bool

Morph the source estimates to the common_brain (default False).

mask : bool | str

Discard data that is labelled ‘unknown’ by the parcellation (only applies to NDVars, default False).

data_raw : bool

Keep the mne.io.Raw instance in ds.info['raw'] (default False).

vardef : str

Name of a test defining additional variables.

decim : int

Override the epoch decim factor.

pad : scalar

Pad the epochs with this much time (in seconds; e.g. for spectral analysis).

ndvar : bool

Add the source estimates as NDVar named “src” instead of a list of mne.SourceEstimate objects named “stc” (default True).

reject : bool | ‘keep’

Reject bad trials. If True (default), bad trials are removed from the Dataset. Set to False to ignore the trial rejection. Set reject='keep' to load the rejection (added it to the events as 'accept' variable), but keep bad trails.

…

Applicable State Parameters:

  • raw: preprocessing pipeline
  • epoch: which events to use and time window
  • rej (trial rejection): which trials to use
  • cov: covariance matrix for inverse solution
  • src: source space
  • inv: inverse solution
Returns:
epochs_dataset : Dataset

Dataset containing single trial data (epochs).