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: 32-ch Low Power Neural Recording System, Brain Machine Interface, Low noise amplifier, Implantable Recording System

Abstract

In this paper, a 32-ch low power neural recording system is developed to analyze neural activities. As neural activity recording system plays important role in neuroscience, as well as in the neuroprosthetics devices, treating of neurological disease and recovery of disabilities, research has been developed over several decades. For example, by monitoring neural signals, we can predict the behavior of paralyzed patients or explain 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. Here, we propose a 32-channel low power fully implantable neural signal recording system. Low noise amplifier, low pass filter, analog MUX, and shift register are designed for full system. Experiment results show low noise and frequency response of the system. Also, in the experiment that measured ECG signal, up to 100uV small signal enable to be detected clearly. For the 32-ch neural recording system, only 1.2V supply voltage is needed with a total power consumption of 50uW. The total gain is 54dB with input referred noise of 3uVrms. Bandwidth is 2Hz~500Hz for targeting ECoG signal. The system is designed with a standard 0.18um CMOS technology, to measure the neural signal with 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). https://doi.org/10.23075/jicas.2021.7.2.003
Section
Articles