A Multi-Channel Neural Recording System With Neural Spike Scan and Adaptive Electrode Selection for High-Density Neural Interface

Han Sol Lee, Kyeongho Eom, Minju Park, Seung Beom Ku, Kwonhong Lee, Taewoo Kim, Taekyung Kim, Young Min Shon, Hangue Park, Hyung Min Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

There is an increasing demand for real-time neural signal monitoring from a large number of electrodes to provide adequate spatial and temporal resolution for high-density brain neural interfaces. This paper proposes an adaptive multi-channel neural recording system that can record neural signals from a large number of electrodes using a smaller number of recording channels. The system utilizes an adaptive electrode selection technique to automatically scan the electrodes where neural spikes occur and record those selected electrodes. A proposed 32-electrode neural recording prototype, including 12 recording channels and 8 scanning channels, was fabricated in a 180-nm CMOS process and tested in vitro. With pre-recorded neural data, the system verified that the occurrence of neural spikes in specific electrodes could be detected and processed in real-time, enabling automatic electrode selection. Measured results showed that the proposed system could lead to over 40% reduction in silicon area compared to conventional works.

Original languageEnglish
Pages (from-to)2844-2857
Number of pages14
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume70
Issue number7
DOIs
Publication statusPublished - 2023 Jul 1

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.

Keywords

  • Adaptive electrode selection
  • implantable device
  • multi-unit activity (MUA)
  • neural interface
  • neural recording

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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