Reinforcement Learning-Based Focality Optimization for Multi-Electrode Temporal Interference Stimulation

Xiayu Chen

doi:10.1109/TBME.2025.3624869

2025-10-23

PRE

Muhammad Mohsin Pathan

DOI：10.1109/TBME.2025.3624869

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摘要

En 中文

Objective: Multi-electrode Temporal Interference Stimulation (TIS) is a promising noninvasive technology for deep brain stimulation, but its clinical translation is hindered by the challenge of optimizing its high-dimensional and hybrid parameter space. Existing algorithms are often limited by high computational cost or a lack of practical flexibility. This paper introduces and validates a novel framework to overcome these barriers. Methods: We developed a novel reinforcement learning (RL) framework that simultaneously optimizes the discrete positions and continuous intensities of stimulation electrodes. We systematically evaluated the framework's performance using six realistic finite element head models and benchmarked it against genetic algorithms (GA) and unsupervised neural networks (USNN). Results: Our RL-based approach significantly enhanced stimulation focality, outperforming GA-based optimization. While achieving performance comparable to USNN, our framework offered the critical advantage of explicit control over the number of active electrodes. Furthermore, our findings reveal that focality improves with an increasing number of electrodes up to 16, with diminishing returns beyond this point. Conclusion: This work establishes a powerful and flexible computational paradigm for TIS optimization. Significance: Our framework provides a crucial guideline for optimal system design and overcomes key limitations of previous methods. This paves the way for more precise and clinically robust noninvasive neuromodulation, accelerating the clinical translation of TIS.

Keyword:

Focality

multi-electrode stimulation

neuromodulation

reinforcement learning

temporal interference stimulation

期刊

IEEE Transactions on Biomedical Engineering

IF:

4.5

论文数:

468

被引数:

2.8W

机构

University of Science and Technology of China

学者数:

1.7W

论文数: 6.0K

被引数: 11.3W

引用论文

Non-invasive temporal interference electrical stimulation of the human hippocampus 人海马的非侵入性时间干扰电刺激

Nature Neuroscience

IF0

2023-10-19

OAAI

Ines R. Violante; Ketevan Alania; Antonino M. Cassarà; Esra Neufeld; Emma Acerbo; Romain Carron; Adam Williamson; Danielle L. Kurtin; Edward Rhodes; Adam Hampshire; Niels Kuster; Edward S. Boyden; Alvaro Pascual-Leone; Nir Grossman

Individually customized transcranial temporal interference stimulation for focused modulation of deep brain structures: a simulation study with different head models

SCIENTIFIC REPORTS

IF3.9

2020-07-16