AI in Medicine

Towards Real-Time Simulations Of Induced Electric Fields During Brain Stimulation Using Conditioned Transformers

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Abstract

Real-time simulations of the induced electric fields during transcranial magnetic stimulation play an important role in guiding and optimizing the coil positioning. In this paper, we present our ongoing work on a deep learning-based surrogate model designed to rapidly predict the induced electric field distribution across the entire cortex, offering a much faster alternative to traditional numerical solvers. Leveraging (conditioned) transformer architectures, our approach operates directly on mesh-based head geometries, achieving highly accurate simulations in just 0.08 seconds on consumer hardware. While we continue to improve the neural surrogate, its current accuracy and efficiency have already enabled integration into an augmented reality platform, demonstrating a promising foundation for live electric field-guided brain stimulation applications.

How to Cite:

Greifeneder, F., Freinberger, D. & Moser, P., (2026) “Towards Real-Time Simulations Of Induced Electric Fields During Brain Stimulation Using Conditioned Transformers”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 2-5.