eelbrain.pipeline.MneExperiment.load_evoked

MneExperiment.load_evoked(subjects=None, baseline=False, ndvar=True, cat=None, samplingrate=None, decim=None, data_raw=False, vardef=None, data='sensor', **kwargs)

Load a Dataset with the evoked responses for each subject.

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) – Apply baseline correction using this period. True to use the epoch’s baseline specification. The default is to not apply baseline correction.

• ndvar (bool | 2) – Convert the mne.Evoked objects to an NDVar (the name in the Dataset is 'meg' or 'eeg'). With ndvar=False, the mne.Evoked objects are added as 'evoked'. 2 to add both.

• cat (sequence of cell-names) – Only load data for these cells (cells of model).

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

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

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

• vardef (str) – Name of a test defining additional variables.

• data (str) – Data to load; ‘sensor’ to load all sensor data (default); ‘sensor.rms’ to return RMS over sensors. Only applies to NDVar output.

• .. –

  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

Notes

Channel interpolation: Bad channels are always interpolated. When loading data for a single subject, bad channels are marked as bad/excluded. When loading group level data, datasets are merged using interpolated data.