An Event-Driven SoC for Neural Recording
File(s)2016_BioCAS_NGNI64_camera.pdf (1.04 MB)
Accepted version
Author(s)
Luan, S
Liu, Y
Williams, I
Constandinou, TG
Type
Conference Paper
Abstract
This paper presents a novel 64-channel ultra-low power/low noise neural recording System-on-Chip (SoC) featuring a highly reconfigurable Analogue Front-End (AFE) and block-selectable data-driven output. This allows a tunable bandwidth/sampling rate for extracting Local Field Potentials (LFPs)
and/or Extracellular Action Potentials (EAPs). Realtime spike detection utilises a dual polarity simple threshold to enable an event driven output for neural spikes (16-sample window). The 64-channels are organised into 16 sets of 4-channel recording blocks, with each block having a dedicated 10-bit SAR ADC that is time division multiplexed among the 4 channels. Each
channel can be individually powered down and configured for bandwidth, gain and detection threshold. The output can thus combine continuous-streaming and event-driven data packets with the system configured as SPI slave. The SoC is implemented in a commercially-available 0.35u m CMOS technology occupying a silicon area of 19.1mm^2 (0.3mm^2 gross per channel) and requiring 32uW/channel power consumption (AFE only).
and/or Extracellular Action Potentials (EAPs). Realtime spike detection utilises a dual polarity simple threshold to enable an event driven output for neural spikes (16-sample window). The 64-channels are organised into 16 sets of 4-channel recording blocks, with each block having a dedicated 10-bit SAR ADC that is time division multiplexed among the 4 channels. Each
channel can be individually powered down and configured for bandwidth, gain and detection threshold. The output can thus combine continuous-streaming and event-driven data packets with the system configured as SPI slave. The SoC is implemented in a commercially-available 0.35u m CMOS technology occupying a silicon area of 19.1mm^2 (0.3mm^2 gross per channel) and requiring 32uW/channel power consumption (AFE only).
Date Issued
2017-01-26
Date Acceptance
2016-08-10
Citation
IEEE Biomedical Circuits and Systems (BioCAS) Conference, 2017, pp.404-407
Publisher
IEEE
Start Page
404
End Page
407
Journal / Book Title
IEEE Biomedical Circuits and Systems (BioCAS) Conference
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
Grant Number
EP/I000569/1
EP/K015060/1
RES/0560/7386 & EFXD12018
EP/M020975/1
BH141353 (EP/M025977/1)
Source
IEEE Biomedical Circuits and Systems (BioCAS) Conference
Subjects
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Biomedical
Engineering, Electrical & Electronic
Computer Science
Engineering
Publication Status
Published
Start Date
2016-10-17
Finish Date
2016-10-19
Coverage Spatial
Shanghai, China