eelbrain.pipeline.MneExperiment.load_evoked_stc

MneExperiment.load_evoked_stc(subjects=None, baseline=True, src_baseline=False, cat=None, keep_evoked=False, morph=None, 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 if group is specified).

  • baseline (bool | Tuple[float | None, float | None]) – Apply baseline correction using this period in sensor space. True to use the epoch’s baseline specification. The default is True.

  • src_baseline (bool | Tuple[float | None, float | None]) – 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[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, except when loading multiple subjects and ndvar=True).

  • mask (bool | str) – Discard data that is labelled unknown by the parcellation. Parcellation name (str) to specify a parcellation, True to use the parc/mask (parcellations) state parameter. Only applies when ndvar=True, 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.

  • samplingrate (int) – Samplingrate in Hz for the analysis (default is specified in epoch definition).

  • decim (int) – Data decimation factor (alternative to samplingrate).

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

  • ... –

    Applicable State Parameters: