eelbrain.load.mne.epochs_ndvar
- eelbrain.load.mne.epochs_ndvar(epochs, name=None, data=None, exclude='bads', mult=1, info=None, sensors=None, vmax=None, sysname=None, connectivity=None, proj=True)
Convert an
mne.Epochs
object to anNDVar
.- Parameters:
epochs (BaseEpochs | Path | str) – The epochs object or path to an epochs FIFF file.
name (str) – Name for the NDVar.
data (Literal['eeg', 'mag', 'grad']) – Which data channels data to include (default based on channels in data).
exclude (str | Sequence[str]) – Channels to exclude (
mne.pick_types()
kwarg). If ‘bads’ (default), exclude channels in info[‘bads’]. If empty do not exclude any.mult (float) – multiply all data by a constant.
info (None | dict) – Additional contents for the info dictionary of the NDVar.
sensors (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 (float) – Set a default range for plotting.
sysname (str) – Name of the sensor system to load sensor connectivity (e.g. ‘neuromag306’, 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 connectionslist 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.proj (bool) – Add projectors (only applies when
epochs
is a path).