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Algorithm/Architecture Co-optimisation Technique for Automatic Data Reduction of Wireless Read-Out in High-Density Electrode Arrays

Published: 22 May 2018 Publication History

Abstract

High-density electrode arrays used to read out neural activity will soon surpass the limits of the amount of data that can be transferred within reasonable energy budgets. This is true for wired brain implants when the required bandwidth becomes very high, and even more so for untethered brain implants that require wireless transmission of data. We propose an energy-efficient spike data extraction solution for high-density electrode arrays, capable of reducing the data to be transferred by over 85%. We combine temporal and spatial spike data analysis with low implementation complexity, where amplitude thresholds are used to detect spikes and the spatial location of the electrodes is used to extract potentially useful sub-threshold data on neighboring electrodes. We tested our method against a state-of-the-art spike detection algorithm, with prohibitively high implementation complexity, and found that the majority of spikes are extracted reliably. We obtain further improved quality results when ignoring very small spikes below 30% of the voltage thresholds, resulting in 91% accuracy. Our approach uses digital logic and is therefore scalable with an increasing number of electrodes.

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  • (2022)Topological Modeling and Parallelization of Multidimensional Data on Microelectrode Arrays2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS53621.2022.00082(795-805)Online publication date: May-2022

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    cover image ACM Transactions on Embedded Computing Systems
    ACM Transactions on Embedded Computing Systems  Volume 17, Issue 3
    May 2018
    309 pages
    ISSN:1539-9087
    EISSN:1558-3465
    DOI:10.1145/3185335
    Issue’s Table of Contents
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    Publication History

    Published: 22 May 2018
    Accepted: 01 January 2018
    Revised: 01 October 2017
    Received: 01 April 2017
    Published in TECS Volume 17, Issue 3

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    Author Tags

    1. Neural probes
    2. data reduction
    3. digital design
    4. embedded systems
    5. low energy

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    • (2022)Topological Modeling and Parallelization of Multidimensional Data on Microelectrode Arrays2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS53621.2022.00082(795-805)Online publication date: May-2022

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