A Wireless Neural Signal Recording System for Implantable Brain Computer Interface

In this paper, a wireless neural signal recording system is proposed for the generation, amplification, digitization and wireless transmission of neural signals through head phantom. The system includes a neural signal generator, a neural signal recording module and a backscatter wireless communication module. The communication module uses amplitude-shift-keying (ASK) modulation. An attenuator is used to mimic the path-loss through seven-layer human brains. In the measurement, the neural signals are successfully demodulated at a receiver. The tested wireless communication rate is $\mathrm{1 ~ M b} / \mathrm{s}$. The system provides a low-cost method to verify the capability of wireless communication.

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Publication Details

Published
2024-09-22
DOI
https://doi.org/10.1109/apcap62011.2024.10882057
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
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article

A Wireless Neural Signal Recording System for Implantable Brain Computer Interface

Yong Jin, Feng Han Lin, Xu Wang
EEG and Brain-Computer Interfaces
article

A Wireless Neural Signal Recording System for Implantable Brain Computer Interface

Yong Jin, Feng Han Lin, Xu Wang
article en

Abstract

In this paper, a wireless neural signal recording system is proposed for the generation, amplification, digitization and wireless transmission of neural signals through head phantom. The system includes a neural signal generator, a neural signal recording module and a backscatter wireless communication module. The communication module uses amplitude-shift-keying (ASK) modulation. An attenuator is used to mimic the path-loss through seven-layer human brains. In the measurement, the neural signals are successfully demodulated at a receiver. The tested wireless communication rate is $\mathrm{1 ~ M b} / \mathrm{s}$. The system provides a low-cost method to verify the capability of wireless communication.

ShanghaiTech University (CN)
Openalex Percentile: Top 73%
EEG and Brain-Computer Interfaces
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