Flexible Electromagnetic-Lens for Wireless, Implantable Brain Machine Interfaces

Wireless power transfer has emerged as an essential requirement in implantable brain-machine interfaces (BMIs), providing significant advantages over traditional wired interfaces. However, challenges such as poor wireless power transfer efficiency, limited coverage area and transmission distance, safety, and interference with wireless communication hinder the widespread adoption of wireless power transfer in implantable BMIs. This paper demonstrates that two types of flexible electromagnetic lenses (EM-lens), which is an array of relay resonators formed as metamaterials, can significantly improve power transfer efficiency (PTE). The circular split ring resonator (C-SRR) based EM-lens outperforms in ex-vivo experiments with a $\mathbf{1 2. 0 7} \mathrm{dB}$ PTE enhancement from $\mathbf{0. 0 6 \%}$ to $\mathbf{1. 0 3 \%}$. The square resonator based EM-lens outperforms in air, improving the PTE from $\mathbf{0. 6 6 \%}$ to $\mathbf{1 6. 5 8 \%}$. Additionally, EM-lenses placed at two different locations in the subcutaneous and epidural regions are simulated in HFSS to determine the optimal placement. The simulations indicate that epidural placement could achieve higher PTE $\mathbf{4. 5 3 \%}$ but with increased specific absorption rate (SAR) as a trade-off. Finally, measurements of PTE versus location misalignment show that the EM-lens can maintain power coverage up to 88.58% of the lens area; and improve the power transmission distance by a factor of $\mathbf{5 3. 8 5 \%}$ compared to a single transmitter configuration.

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Publication Details

Published
2024-10-24
DOI
https://doi.org/10.1109/biocas61083.2024.10798285
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.34

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article

Flexible Electromagnetic-Lens for Wireless, Implantable Brain Machine Interfaces

Timothy G. Constandinou, Berkay Özbek, Peilong Feng, Andrea Mifsud et al.
1 citations
EEG and Brain-Computer Interfaces
0.34
article

Flexible Electromagnetic-Lens for Wireless, Implantable Brain Machine Interfaces

Timothy G. Constandinou, Berkay Özbek, Peilong Feng, Andrea Mifsud, Jianjun Ge
article en
1 citations

Abstract

Wireless power transfer has emerged as an essential requirement in implantable brain-machine interfaces (BMIs), providing significant advantages over traditional wired interfaces. However, challenges such as poor wireless power transfer efficiency, limited coverage area and transmission distance, safety, and interference with wireless communication hinder the widespread adoption of wireless power transfer in implantable BMIs. This paper demonstrates that two types of flexible electromagnetic lenses (EM-lens), which is an array of relay resonators formed as metamaterials, can significantly improve power transfer efficiency (PTE). The circular split ring resonator (C-SRR) based EM-lens outperforms in ex-vivo experiments with a $\mathbf{1 2. 0 7} \mathrm{dB}$ PTE enhancement from $\mathbf{0. 0 6 \%}$ to $\mathbf{1. 0 3 \%}$. The square resonator based EM-lens outperforms in air, improving the PTE from $\mathbf{0. 6 6 \%}$ to $\mathbf{1 6. 5 8 \%}$. Additionally, EM-lenses placed at two different locations in the subcutaneous and epidural regions are simulated in HFSS to determine the optimal placement. The simulations indicate that epidural placement could achieve higher PTE $\mathbf{4. 5 3 \%}$ but with increased specific absorption rate (SAR) as a trade-off. Finally, measurements of PTE versus location misalignment show that the EM-lens can maintain power coverage up to 88.58% of the lens area; and improve the power transmission distance by a factor of $\mathbf{5 3. 8 5 \%}$ compared to a single transmitter configuration.

Imperial College London (GB)
UK Dementia Research Institute, Medical Research Council, Engineering and Physical Sciences Research Council
Affordable and clean energy
Openalex Percentile: Top 37%
EEG and Brain-Computer Interfaces
0.34
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Flexible Electromagnetic-Lens for Wireless, Implantable Brain Machine Interfaces — Timothy G. Constandinou, Berkay Özbek, et al. · (2024) | TGRS Research Map | TGRS