MEP001 - Integrating NiiVue as plotting backend for visualization requiring meshes and volumes

Describe the new feature or enhancement

Following a discussion with @larsoner, I am proposing this "MNE-Python Enhancement Proposal" for discussion. The implementation of this work would be driven and supported by a 2-year supplement to a current NIH-R01 (#1RF1MH133701-01; PI: Chris Rorden). The submission deadline for the supplement is November 30th.

The parent R01 is on the NiiVue project and its Python wrapper IPyNiiVue. A crude first version of the objectives of the supplement (these may shift as the writing of the proposal progresses) is:

• Aim 1: Improve MNE-BIDS and EEGNET to support derivatives BIDS datasets of EEG/MEG source data.
• Aim 2: Integrate NiiVue as a backend for MNE-Python plotting functions.
• Aim 3: Integrate NiiVue as a Plotly Dash component for easy reuse in Python web dashboards.

Directly relevant to this MEP, is the aim 2. I provide the full list of three aims just because they may end up interacting with aim 2.

Describe your proposed implementation

In short, through this MEP, I would like to propose better integrating the NiiVue visualizer with MNE-Python for EEG/MEG processing by adding it to the supported backend for the plotting functions working with volumes and meshes. I think this viewer will have multiple advantages, the main one for me being its integration with web technologies, freeing MNE plotting functions from dependencies on C++ bindings (e.g., QT) which often causes installation issues.

Describe possible alternatives

NiiVue could be integrated using the already established plotting backend abstraction layer (mne/viz/backends/*.py) and the IPyNiiVue approach. Alternatively, it could also be first wrapped as a plotly component, and plotly support could be more thoroughly integrated. A small demo of Plotly integration was provided a while back https://mne.tools/mne-dash/. I think we pushed this concept much further with the Quality Control Review board we coded for PyLossless. I think these efforts can be synergized to offer much better dashboard capability in MNE-Python.

A measure of success would be the ability to easily plot (i.e., not with 20 lines of codes loading volumes, meshes, co-registering them, and all, but with something like a one-liner) source results in virtualized environments like the Google Colab. This would make teaching, training, and research with MNE-Python significantly easier.

Additional context

No response