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Compact standalone platform for neural recording with real-time spike sorting and data logging

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Luan_2018_J._Neural_Eng._15_046014.pdfPublished version2.87 MBAdobe PDFView/Open
Title: Compact standalone platform for neural recording with real-time spike sorting and data logging
Authors: Luan, S
Williams, I
Maslik, M
Liu, Y
De Carvalho, F
Jackson, A
Quian Quiroga, R
Constandinou, T
Item Type: Journal Article
Abstract: Objective. Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective Brain Machine Interfaces (BMIs). These recordings generate enormous amounts of data for transmission & storage, and typically require o ine processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: 1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); 2) producing real-time, low-latency, spike sorted data; and 3) long term untethered operation. Approach. We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data. Main results. The system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24 hours at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24-hour initial recording in a nonhuman primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles. Signi cance The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals { revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and veri able output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.
Issue Date: 1-Aug-2018
Date of Acceptance: 6-Apr-2018
URI: http://hdl.handle.net/10044/1/57978
DOI: 10.1088/1741-2552/aabc23
ISSN: 1741-2552
Publisher: IOP Publishing
Start Page: 1
End Page: 13
Journal / Book Title: Journal of Neural Engineering
Volume: 15
Issue: 4
Copyright Statement: © 2018 IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
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
Engineering & Physical Science Research Council (E
Funder's Grant Number: EP/I000569/1
EP/K015060/1
EP/I000569/1
EP/K503733/1
EP/M020975/1
EP/R511547/1
RES/0560/7386 & EFXD12018
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Neurosciences
Engineering
Neurosciences & Neurology
neural recording
spike sorting
spike detection
template matching
real-time
chronic
logging
COMPRESSION
ALGORITHMS
FUTURE
CORTEX
Action Potentials
Animals
Computer Systems
Data Interpretation, Statistical
Haplorhini
Neurons
Printing, Three-Dimensional
Signal Processing, Computer-Assisted
Neurons
Animals
Haplorhini
Data Interpretation, Statistical
Action Potentials
Computer Systems
Signal Processing, Computer-Assisted
Printing, Three-Dimensional
Biomedical Engineering
0903 Biomedical Engineering
1103 Clinical Sciences
1109 Neurosciences
Publication Status: Published
Article Number: 046014
Online Publication Date: 2018-05-15
Appears in Collections:Electrical and Electronic Engineering
Faculty of Engineering