eelbrain.load.fiff.epochs_ndvar(epochs, name=None, data=None, exclude='bads', mult=1, info=None, sensors=None, vmax=None, sysname=None, connectivity=None)

Convert an mne.Epochs object to an NDVar.

epochs : mne.Epochs | str

The epochs object or path to an epochs FIFF file.

name : None | str

Name for the NDVar.

data : ‘eeg’ | ‘mag’ | ‘grad’

Which data channels data to include (default based on channels in data).

exclude : list of string | str

Channels to exclude (mne.pick_types() kwarg). If ‘bads’ (default), exclude channels in info[‘bads’]. If empty do not exclude any.

mult : scalar

multiply all data by a constant.

info : None | dict

Additional contents for the info dictionary of the NDVar.

sensors : None | Sensor

The default (None) reads the sensor locations from the fiff file. If the fiff file contains incorrect sensor locations, a different Sensor can be supplied through this kwarg.

vmax : None | scalar

Set a default range for plotting.

sysname : str

Name of the sensor system to load sensor connectivity (e.g. ‘neuromag’, inferred automatically for KIT data converted with a recent version of MNE-Python).

connectivity : str | list of (str, str) | array of int, (n_edges, 2)

Connectivity between elements. Can be specified as:

  • "none" for no connections
  • list of connections (e.g., [('OZ', 'O1'), ('OZ', 'O2'), ...])
  • numpy.ndarray of int, shape (n_edges, 2), to specify connections in terms of indices. Each row should specify one connection [i, j] with i < j. If the array’s dtype is uint32, property checks are disabled to improve efficiency.
  • "grid" to use adjacency in the sensor names

If unspecified, it is inferred from sysname if possible.