Multi-Channel Temporal Interference Retinal Stimulation Based on Reinforcement Learning

Retinal degenerative diseases such as age-related macular degeneration and retinitis pigmentosa cause severe vision impairment, while current electrical stimulation therapies are limited by poor spatial targeting precision. As a promising non-invasive alternative, the efficacy of temporal interference stimulation (TIS) for retinal targeting depends on optimized multi-electrode parameters. This study reconstructed a whole-head finite element model with detailed ocular structures and applied reinforcement learning (RL)-based multi-channel electrode parameter optimization to retinal stimulation. Systematic evaluation demonstrated that the focal precision of TIS improves with increasing channel numbers (consistent across all subject head models), with RL significantly outperforming conventional genetic algorithms (GA) and unsupervised neural networks (USNN) in focusing capability. Furthermore, by implementing the computationally intensive envelope calculation using the JAX framework, we achieved a nearly order-of-magnitude reduction in optimization time (to approx. 2 minutes per run on an RTX 4090D), significantly enhancing the practical feasibility of the proposed RL framework. This work provides a novel and computationally efficient methodology for precise non-invasive neuromodulation parameter optimization, applicable not only to retinal diseases but potentially to broader neurological conditions.