Efficient super-resolution Bayesian eletromagnetic brain imaging

Electromagnetic source imaging at superresolution presents a significant challenge, requiring the estimation of several thousand parameters of complex brain activity from a limited number of sensor data. Sparse Bayesian learning offers robustness in reconstructing complex sources compared to classical methods. However, existing Bayesian approaches for super-resolution brain imaging suffer from 1) computational inefficiency due to numerous hyperparameters and iterations, and 2) reliance on arbitrary thresholds for determining active brain sources. This paper introduces a robust and efficient Bayesian approach for reconstructing brain sources and noise at superresolution. Our method incorporates hyperparameter pruning during optimization, where near-zero hyperparameters are dynamically removed to accelerate convergence. This pruning strategy simultaneously improves computational efficiency and determines the sparsity ratio of brain source activity, eliminating the need for arbitrary thresholds. Our algorithm successfully reconstructs complex brain source and noise activities at super-resolution under both Gaussian and real-world noise conditions with statistically significant reconstruction accuracy and runtime efficiency when compared to established benchmark reconstruction algorithms (beamformers, sLORETA etc.) both for simulations and for real data. Importantly, our algorithm achieves efficient reconstruction of complex brain activity, resolving distinct and functionally relevant brain areas even with limited trials in real MEG data, with statistically significant performance improvements when compared to benchmarks. These results demonstrate the feasibility, accuracy, and reliability of super-resolution electromagnetic brain imaging.