Compact standalone platform for neural recording with real-time spike sorting and data logging
File(s)Luan_2018_J._Neural_Eng._15_046014.pdf (2.8 MB)
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Author(s)
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.
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.
Date Issued
2018-08-01
Date Acceptance
2018-04-06
Citation
Journal of Neural Engineering, 2018, 15 (4), pp.1-13
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
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of the work, journal citation and DOI
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License URL
Sponsor
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
Identifier
https://iopscience.iop.org/article/10.1088/1741-2552/aabc23
Grant Number
EP/I000569/1
EP/K015060/1
EP/I000569/1
EP/K503733/1
EP/M020975/1
EP/R511547/1
RES/0560/7386 & EFXD12018
Subjects
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
Date Publish Online
2018-05-15