Data mining and visualisation in neuroscience applications - The CARMEN project and the signal data explorer toolset

Tom Jackson, Martyn Fletcher, Bojian Liang, Mark Jessop, Simon Davidson, Jim Austin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper discusses data-mining and visualisation techniques that are being developed to support neuroscience applications in the analysis of experimental neurophysiological data. The work is part of a large scale neuroinformatics project, CARMEN, which is developing Grid and web-based information repositories to assist in the dissemination and collaborative analysis of neurophysiological data. Central to the project is the provision of flexible, multi-format visualisation tools and data-mining services to extract event information from the high volume electrophysiological, multi-channel time-series datasets that are being stored on the data repositories. The visualisation and search tool being developed and deployed is the Signal Data Explorer (SDE). The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. The search methods that underpin the SDE tool are discussed, as is the distributed architecture which has been deployed to support the concept of collaborative neuroinformatics over Grid and web-service based systems.

Original languageEnglish
Title of host publication6th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2009
PublisherBritish Institute of Non-Destructive Testing
Pages842-853
Number of pages12
Volume2
ISBN (Print)9781618390097
Publication statusPublished - 2009
Event6th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2009 - Dublin, Ireland
Duration: 23 Jun 200925 Jun 2009

Conference

Conference6th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2009
Country/TerritoryIreland
CityDublin
Period23/06/0925/06/09

Keywords

  • Grid
  • Neural networks
  • Neuroinformatics
  • Pattern matching
  • Spike detection

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