MNE Python Scaling Documentation Interactivity via JupyterLite and Interactive Plotting

MNE-Python’s documentation is extensive but primarily static, requiring local installation for users to explore data. This project aims to integrate JupyterLite into the sphinx-gallery build, enabling a "Try in Browser" feature for core tutorials. Key challenges include managing large neuroimaging datasets in a WebAssembly environment and maintaining 3D interactivity (PyVista/Trame). I will deliver a production-ready CI/CD pipeline for JupyterLite builds, a prioritized set of interactive lightweight examples, and a library of ipywidgets for real-time parameter tuning (e.g., filtering, scaling) directly in the documentation.