A leadless power transfer and wireless telemetry solutions for an endovascular electrocorticography
<|reference_start|>A leadless power transfer and wireless telemetry solutions for an endovascular electrocorticography: Endovascular brain-computer interfaces (eBCIs) offer a minimally invasive way to connect the brain to external devices, merging neuroscience, engineering, and medical technology. Achieving wireless data and power transmission is crucial for the clinical viability of these implantable devices. Typically, solutions for endovascular electrocorticography (ECoG) include a sensing stent with multiple electrodes (e.g. in the superior sagittal sinus) in the brain, a subcutaneous chest implant for wireless energy harvesting and data telemetry, and a long (tens of centimetres) cable with a set of wires in between. This long cable presents risks and limitations, especially for younger patients or those with fragile vasculature. This work introduces a wireless and leadless telemetry and power transfer solution for endovascular ECoG. The proposed solution includes an optical telemetry module and a focused ultrasound (FUS) power transfer system. The proposed system can be miniaturised to fit in an endovascular stent. Our solution uses optical telemetry for high-speed data transmission (over 2 Mbit/s, capable of transmitting 41 ECoG channels at a 2 kHz sampling rate and 24-bit resolution) and the proposed power transferring scheme provides up to 10mW power budget into the site of the endovascular implants under the safety limit. Tests on bovine tissues confirmed the system's effectiveness, suggesting that future custom circuit designs could further enhance eBCI applications by removing wires and auxiliary implants, minimising complications.<|reference_end|>
@article{xu2024a,
    title={A leadless power transfer and wireless telemetry solutions for an
  endovascular electrocorticography},
    author={Zhangyu Xu, Majid Khazaee, Nhan Duy Truong, Deniel Havenga, Armin
  Nikpour, Arman Ahnood, Omid Kavehei},
    journal={arXiv preprint arXiv:2405.04806},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04806},
    primaryClass={eess.SY cs.SY}
}