A 32-Channel Low-Power Neural Recording System with Continuously Monitoring for ECoG Signal Detection

  • Jung Woo Jang Seoul National University
  • Yu Ri Kim Seoul National University
  • Chae Eun Lee Seoul National University
  • Yoon-Kyu Song Seoul National University
Keywords: Brain–Machine Interface, Low-Noise Amplifier, Implantable Recording System

Abstract

In this study, a 32-channel low-power neural recording system was developed to analyze neural activity. Neural activity recording systems play an important role in neurosciences as well as the development of neuroprosthetic devices to treat neurological diseases and assist in recovery from disabilities. For example, monitoring neural signals allows prediction of the behaviors of paralyzed patients or explanation for the causality between behavior and neural activity. A conventional recording system with an integrated circuit has several limitations in terms of noise and power consumption. Herein, we propose a 32-channel low-power fully implantable neural signal recording system; a low-noise amplifier, a lowpass filter, an analog multiplexer, and a shift register were designed for the system. The experimental results highlight the low noise and adequate frequency response of the system. Further, in the experiments, ECG signals with magnitudes of up to 100 μV could be detected clearly. For the 32-channel neural recording system, a low supply voltage of only 1.2 V is needed, and the total power consumption is 60 μW, with a total gain of 58 dB and input referred noise of 3 μVrms. The bandwidth of the system is 2–300 Hz for measuring target ECoG signals. The system was designed with a standard 0.18 μm CMOS technology to measure neural signals while maintaining very low power consumption.

Author Biographies

Jung Woo Jang, Seoul National University

Jung Woo Jang received the B.S. degree in Electric Engineering from Konkuk University, Seoul, Korea, in 2013 and is currently working toward Ph. D. degree in Nanoscience and Technology from Seoul National University, Korea. His research interests include BMI system for recording neural signal, wireless data and power transmission, especially ultra-low power and low noise neural recording system.

Yu Ri Kim, Seoul National University

Yu Ri Kim received the B.S. degree in Electric Engineering from Soongsil University, Seoul, Korea, in 2018 and is currently working toward Master. degree in Nanoscience and Technology from Seoul National University, Korea. Her main research interest is designing ultra-low power system for Brain Machine Interface and recording Bio-Impedance.

Chae Eun Lee, Seoul National University

Chae Eun Lee received the B.S degree in Electronics Engineering from Ewha Womans University, in 2017 and is currently working toward Ph.D. degree in Nanoscience and Technology from Seoul National University, Korea. Her main research interest is developing neural stimulators, especially for visual prostheses, and wireless bidirectional power transfer for implantable Brain Machine Interface.

Yoon-Kyu Song, Seoul National University

Yoon-Kyu Song received the B.S. and M.S. degree in Electric engineering form Seoul National University, Korea, in 1992 and 1994, respectively, and the Ph.D. degree from Brown University, Providence, RI, USA in 1999. His research interests include basic and applied semiconductor optoelectronics, such as vertical cavity lasers and nanostructured light emitters.

Homepage : https://nnp.snu.ac.kr/

Published
2021-04-01
How to Cite
Jang, J. W., Kim, Y. R., Lee, C. E., & Song, Y.-K. (2021). A 32-Channel Low-Power Neural Recording System with Continuously Monitoring for ECoG Signal Detection . Journal of Integrated Circuits and Systems, 7(2), 12-16. https://doi.org/10.23075/jicas.2021.7.2.003
Section
Articles