A Whole‐Head Finite Element Model for Electrical Neuromodulation via Visual Brain‐Machine Interfaces

Brain-machine interfaces (BMIs) for vision restoration require models that accurately simulate the anatomy and electrical properties of visual pathways. However, current models focus only on isolated structures, such as the retina or brain, and overlook surrounding tissues. Here, we present a comprehensive computational model of the human head that incorporates the entire visual pathway-including the eye, optic nerve, and brain-along with critical neighboring tissues such as the orbit and paranasal sinuses, thereby enabling precise simulations. Validation using human and large-animal data shows a strong correlation between the simulated and measured electric potentials. Component-elimination analysis reveals that the optimized comprehensive model outperforms simplified versions. The model demonstrates its utility in multiple applications: (1) comparative analysis of electrical neuromodulation technologies for optic neuropathy, revealing the electric field intensity limitations of noninvasive approaches and the safety concerns of invasive intraorbital approaches; (2) identification of the optimal stimulation site, showing that transnasal stimulation at the optic chiasm outperforms traditional approaches; and (3) in silico design of electrode arrays for optic nerve prostheses, demonstrating theoretical advantages in invasiveness and visual field coverage compared to existing retinal and cortical prosthetics. This validated and versatile computational resource supports the development of neuromodulation strategies and visual BMI technologies.

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Publication Details

Journal
Advanced Science
Published
2026-07-16
DOI
https://doi.org/10.1002/advs.202511252
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.00

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article

A Whole‐Head Finite Element Model for Electrical Neuromodulation via Visual Brain‐Machine Interfaces

Tonghe Yang, HL Wu, Yikui Zhang, Y Cao et al.
Advanced Science
Neuroscience and Neural Engineering
article

A Whole‐Head Finite Element Model for Electrical Neuromodulation via Visual Brain‐Machine Interfaces

Tonghe Yang, HL Wu, Yikui Zhang, Y Cao, Wu Wh, Jie Yang, Geng Yuan, W N Yan, S Hücümenog ̆ lu, Yangrui Huang, Haodi Chen, Shurui Huang, Te Zheng
article en

Abstract

Brain-machine interfaces (BMIs) for vision restoration require models that accurately simulate the anatomy and electrical properties of visual pathways. However, current models focus only on isolated structures, such as the retina or brain, and overlook surrounding tissues. Here, we present a comprehensive computational model of the human head that incorporates the entire visual pathway-including the eye, optic nerve, and brain-along with critical neighboring tissues such as the orbit and paranasal sinuses, thereby enabling precise simulations. Validation using human and large-animal data shows a strong correlation between the simulated and measured electric potentials. Component-elimination analysis reveals that the optimized comprehensive model outperforms simplified versions. The model demonstrates its utility in multiple applications: (1) comparative analysis of electrical neuromodulation technologies for optic neuropathy, revealing the electric field intensity limitations of noninvasive approaches and the safety concerns of invasive intraorbital approaches; (2) identification of the optimal stimulation site, showing that transnasal stimulation at the optic chiasm outperforms traditional approaches; and (3) in silico design of electrode arrays for optic nerve prostheses, demonstrating theoretical advantages in invasiveness and visual field coverage compared to existing retinal and cortical prosthetics. This validated and versatile computational resource supports the development of neuromodulation strategies and visual BMI technologies.

Advanced Science
Beijing Institute of Technology (CN), Wenzhou Medical University (CN), Affiliated Eye Hospital of Wenzhou Medical College (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 13%
Neuroscience and Neural Engineering
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