A Low-Noise, Wireless, Frequency-Shaping Neural Recorder

Jian Xu, Anh Tuan Nguyen, Wenfeng Zhao, Hongsun Guo, Tong Wu, Harvey Wiggins, Edward W. Keefer, Hubert Lim, Zhi Yang

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

This paper presents a low-noise, wireless neural recorder that has a frequency dependent amplification to remove electrode offset and to attenuate motion artifacts. The recorder has 2.5 GΩ and 50 MΩ input impedance at 20 Hz and 1 kHz for recording local field potentials and extracellular spikes, respectively. To reduce the input-referred noise, we propose a low-noise frontend design with multiple novel noise suppression techniques. To reduce the power consumption, we have integrated an exponential component and polynomial component spike processor that automatically adjusts the recording bandwidth based on the signal contents. In bench-top measurement, the proposed neural recorder has 2.2- μ V input-referred noise integrated from 300 Hz to 8 kHz and consumes 98- μ W maximum power. In animal experiments, the output data of the neural signal processor are serialized and connected to a customized WiFi data link with up to 10 Mbps data rate. Through in vivo experiments, we find that the noise generated by the WiFi does not prevent brain recordings with microelectrodes and a clear interpretation of the neural signals; however, the noise can mask the weaker neural signals in nerve recordings with epineural electrodes.

Original languageEnglish (US)
Pages (from-to)187-200
Number of pages14
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
Volume8
Issue number2
DOIs
StatePublished - Jun 2018

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Keywords

  • Brain recording
  • frequency-shaping recorder
  • nerve recording
  • neural spike processor
  • wireless neural data acquisition

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