eelbrain.load.fiff.epochs_ndvar¶
-
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 anNDVar
.Parameters: - 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.