A new system architecture for future long-term high-density neural recording

Jian Xu, Tony Wu, Zhi Yang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

This brief presents a new system architecture for neural recording to allow higher recording density and more tolerance to interface degeneration and artifacts. Compared with its conventional counterpart, the proposed architecture has a frequency-dependent gain stage that inherently rejects dc offset and attenuates low-frequency interferences. In the digital domain, frequency compensation is used to restore the signals 'seen' by an electrode. Powered by a switched-capacitor design, the proposed architecture can lead to major improvements on system performance metrics, including input impedance, distortion, and dynamic range. In simulations with different electrode sizes and degeneration levels, the proposed architecture consistently gives high-fidelity recording data. We argue that the proposed architecture is more suitable for long-term high-density invasive brain-computer interface experiments as a replacement to better support a mimicked 'Moore's Law' on recording density.

Original languageEnglish (US)
Article number6519945
Pages (from-to)403-406
Number of pages4
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume60
Issue number7
DOIs
StatePublished - May 30 2013

Keywords

  • Dynamic range (DR)
  • frequency shaping
  • input impedance
  • neural recording

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