Gehring, T., Vasilaki, E. and Giugliano, M. (2015) Highly Scalable Parallel Processing of Extracellular Recordings of Multielectrode Arrays. In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. 37th Annual International Conference of the IEEE, 25-29 Aug 2015, Milan, Italy. IEEE
Abstract
Technological advances of Multielectrode Arrays (MEAs) used for multi- site, parallel electrophysiological recordings, lead to an ever increasing amount of raw data being generated. Arrays with hundreds up to a few thousands of electrodes are slowly seeing widespread use and the expectation is that more sophisticated arrays will become available in the near future. In order to process the large data volumes resulting from MEA recordings there is a pressing need for new software tools able to process many data channels in parallel. Here we present a new tool for processing MEA data recordings that makes use of new programming paradigms and recent technology developments to unleash the power of modern highly parallel hardware, such as multi-core CPUs with vector instruction sets or GPGPUs. Our tool builds on and complements existing MEA data analysis packages. It shows high scalability and can be used to speed up some performance critical pre-processing steps such as data filtering and spike detection, helping to make the analysis of larger data sets tractable.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Feb 2016 13:03 |
Last Modified: | 19 Dec 2022 13:32 |
Published Version: | http://dx.doi.org/10.1109/EMBC.2015.7319315 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/EMBC.2015.7319315 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:90138 |