invertmeeg: A Benchmark and Unified Python Library for EEGInverse Solvers

Abstract

Electroencephalography (EEG) source imaging remains difficult to compare systematically because inverse solvers are distributed across different software packages, programming languages, and evaluation protocols. We present a frozen four-scenario EEG benchmark of 106 solvers evaluated on a shared BioSemi-32 / ico3 setup, together with invertmeeg, an open-source Python package that currently exposes 118 inverse solvers through a consistent two-step interface built on the MNE-Python ecosystem. The benchmark spans focal, multi-source, spatially extended, and low-SNR source configurations and uses earth movers distance (EMD) as the primary metric, with average precision (AP), mean localization error (MLE), and correlation used for complementary ranking. Across this benchmark, no single solver dominates every regime: flexible subspace and hybrid methods perform best overall, while Bayesian methods remain particularly competitive under extended-source and low-SNR conditions. The package is available via pip install invertmeeg and imported in Python as invert.