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 an NDVar.

  • 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. ‘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 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.


If evoked objects have different channels, the intersection is used (i.e., only the channels present in all objects are retained).