eelbrain.load.fiff.evoked_ndvar¶
-
eelbrain.load.fiff.
evoked_ndvar
(evoked, name=None, data=None, exclude='bads', vmax=None, sysname=None, connectivity=None)¶ Convert one or more mne
Evoked
objects to anNDVar
.Parameters: - evoked : str | Evoked | list of Evoked
The Evoked to convert to NDVar. Can be a string designating a file path to a evoked fiff file containing only one evoked.
- name : str
Name of the NDVar.
- data : ‘eeg’ | ‘mag’ | ‘grad’
Which data channels data to include (default based on channels in data).
- exclude : list of string | string
Channels to exclude (
mne.pick_types()
kwarg). If ‘bads’ (default), exclude channels in info[‘bads’]. If empty do not exclude any.- 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.
Notes
If evoked objects have different channels, the intersection is used (i.e., only the channels present in all objects are retained).