TY - GEN
T1 - Wireless Fully-passive Neural Recorder with Artifact Reduction by Optical Chopping
AU - Liu, Shiyi
AU - Gulick, Daniel
AU - Benbuk, Ahmed Abed
AU - Christen, Jennifer Blain
N1 - Funding Information:
Research reported in this publication was supported by HHS-NIH: National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under award number R21 EB028396. The authors would like to acknowledge Prof. Junseok Chae as we sadly continue this work without him.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Long-term continuous wireless monitoring of neural signals enables early detection of neurological disorders without restricting the patient's mobility. Existing wireless neural recording systems cannot effectively address the technical challenges associated with long-term implantation due to their large size, high power consumption, and high failure rate. In this paper, we present a miniaturized fully-passive wireless neural recorder thin enough to be implanted under the skull for long-term continuous neural recordings. Based on RF backscattering telemetry, the fully-passive sensor features a diameter of 6 mm, and has a near-zero power consumption. An artifact reduction method based on optical signal chopping was developed to improve the wireless signal integrity at low frequencies. Benchtop characterization verified the neural recorder's capability to accurately measure electrical signals as low as 300 μVpp at a distance of 45 mm. In addition, in vivo verification was conducted by implanting the recorder onto rodent skull surface. Seizure activity was successfully detected using two deep brain electrodes inserted through burr holes. These results demonstrate the potential of our proposed wireless neural recorder for long-term and continuous neural recording applications.
AB - Long-term continuous wireless monitoring of neural signals enables early detection of neurological disorders without restricting the patient's mobility. Existing wireless neural recording systems cannot effectively address the technical challenges associated with long-term implantation due to their large size, high power consumption, and high failure rate. In this paper, we present a miniaturized fully-passive wireless neural recorder thin enough to be implanted under the skull for long-term continuous neural recordings. Based on RF backscattering telemetry, the fully-passive sensor features a diameter of 6 mm, and has a near-zero power consumption. An artifact reduction method based on optical signal chopping was developed to improve the wireless signal integrity at low frequencies. Benchtop characterization verified the neural recorder's capability to accurately measure electrical signals as low as 300 μVpp at a distance of 45 mm. In addition, in vivo verification was conducted by implanting the recorder onto rodent skull surface. Seizure activity was successfully detected using two deep brain electrodes inserted through burr holes. These results demonstrate the potential of our proposed wireless neural recorder for long-term and continuous neural recording applications.
KW - Artifact Reduction
KW - Backscattering
KW - Battery-free
KW - Chopping
KW - Neural Recording
KW - RFID
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U2 - 10.1109/BioCAS54905.2022.9948605
DO - 10.1109/BioCAS54905.2022.9948605
M3 - Conference contribution
AN - SCOPUS:85142926264
T3 - BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings
SP - 55
EP - 59
BT - BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
Y2 - 13 October 2022 through 15 October 2022
ER -