Microwave brain stimulation plarform: neural network-based parameter optimization for non-invasive applications

Abstract

Non-invasive brain stimulation has numerous fundamental challenges in balancing penetration depth and spatial focus. Established methods, such as Transcranial Magnetic

Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS), are effective only on superficial cortical targets. Microwave Brain Stimulation (MBS) emerges as a promising alternative to reach subcortical targets, but its clinical viability critically depends on the complex and multiobjective optimization of the antenna arrangement. This modeling problem, which involves the simultaneous adjustment of physical (hardware) and electrical (beam control) parameters, remains a gap in the literature. This thesis proposes and validates a new computational platform, the Hybrid Coevolutionary Method (HCM-TBS), specifically designed to solve this problem. The platform implements a multi-stage modular architecture that integrates global search algorithms (PSO), diversity and robustness (GA, SA), and a coevolutionary optimization core.This core decomposes the problem into subpopulations of physical (hardware) and electrical (software) parameters, optimizing them cooperatively through joint fitness assessments in an FDTD (Meep) simulator. The performance of the HCM-TBS platform was rigorously validated against baseline methods thru 30 independent runs for each group. The results demonstrate that HCM-TBS achieves statistically superior beamforming accuracy (Directivity) (p < 0.001). Moreover, the analysis reveals that the proposed platform produces solutions with significantly lower variability, demonstrating high robustness and reliability, in contrast to the extreme performance instability observed in the baseline methods. In light of the findings, this thesis contributes a robust and statistically validated computational framework for the modeling of non-invasive neurostimulation systems, prioritizing reliability and precision of focus, essential steps for future clinical translation.