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Autori principali: Jaykar, Kalpesh, Velvaluri, Prasanth, Sun, Nian X., James, Richard D.
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2509.23520
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author Jaykar, Kalpesh
Velvaluri, Prasanth
Sun, Nian X.
James, Richard D.
author_facet Jaykar, Kalpesh
Velvaluri, Prasanth
Sun, Nian X.
James, Richard D.
contents A new brain-computer interface (BCI) technology, deployed through minimally invasive surgery, is changing the way we think about treating severe neurological conditions. The central idea is to place a device called Stentrode in the brain's vasculature, which enables neuromodulation and helps patients regain the ability to communicate. However, in such devices, the battery and electronics are wired and could introduce damage or implant malfunction. In these cases, a Stentrode integrated with magnetoelectric (ME) antennas could be of great interest. ME antennas offer significant advantages over traditional antennas, leveraging acoustic resonance rather than electromagnetic resonance to achieve a size reduction of up to five orders of magnitude. In addition to their compactness and immunity to ground-plane interference, ME antennas could be adopted for use in vascular implants, such as coronary stents, potentially enabling minimally invasive monitoring and communication. Despite these advantages, a single antenna embedded in the implant may be constrained by the limited volume of magnetostrictive material, which could result in low output gain. To address this gain limitation, we propose using antenna arrays designed to produce constructive interference at a designated far-field point, ideally located outside the patient, to enhance signal transmission and receiving capabilities. We develop a mathematical model to represent the antennas and optimize their spatial arrangement and phase synchronization. Simulations based on this model demonstrate promising high-gain performance at the prescribed far-field location through phase manipulation.
format Preprint
id arxiv_https___arxiv_org_abs_2509_23520
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Theoretical framework of passive ME antenna arrays enabling in-vivo monitoring: A pathway to smart implants
Jaykar, Kalpesh
Velvaluri, Prasanth
Sun, Nian X.
James, Richard D.
Signal Processing
A new brain-computer interface (BCI) technology, deployed through minimally invasive surgery, is changing the way we think about treating severe neurological conditions. The central idea is to place a device called Stentrode in the brain's vasculature, which enables neuromodulation and helps patients regain the ability to communicate. However, in such devices, the battery and electronics are wired and could introduce damage or implant malfunction. In these cases, a Stentrode integrated with magnetoelectric (ME) antennas could be of great interest. ME antennas offer significant advantages over traditional antennas, leveraging acoustic resonance rather than electromagnetic resonance to achieve a size reduction of up to five orders of magnitude. In addition to their compactness and immunity to ground-plane interference, ME antennas could be adopted for use in vascular implants, such as coronary stents, potentially enabling minimally invasive monitoring and communication. Despite these advantages, a single antenna embedded in the implant may be constrained by the limited volume of magnetostrictive material, which could result in low output gain. To address this gain limitation, we propose using antenna arrays designed to produce constructive interference at a designated far-field point, ideally located outside the patient, to enhance signal transmission and receiving capabilities. We develop a mathematical model to represent the antennas and optimize their spatial arrangement and phase synchronization. Simulations based on this model demonstrate promising high-gain performance at the prescribed far-field location through phase manipulation.
title Theoretical framework of passive ME antenna arrays enabling in-vivo monitoring: A pathway to smart implants
topic Signal Processing
url https://arxiv.org/abs/2509.23520