About: Correlation-based Neural Gas for Visualizing Correlations between EEG Features     Goto   Sponge   NotDistinct   Permalink

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Description
  • Feature selection is an important issue in an automated data analysis. Unfortunately the majority of feature selection methods does not consider inner relationships between features. Furthermore existing methods are based on a prior knowledge of a data classification. Among many methods for displaying data structure there is an interest in self organizing maps and its modifications. Neural gas network has shown surprisingly good results when capturing the inner structure of data. Therefore we propose its modification (correlation - based neural gas) and we use this network to visualize correlations between features. We discuss the posibility to use this additional information for fully automated unsupervised feature selection where no classification is available. The algorithm is tested on the EEG data acquired during the mental rotation task.
  • Feature selection is an important issue in an automated data analysis. Unfortunately the majority of feature selection methods does not consider inner relationships between features. Furthermore existing methods are based on a prior knowledge of a data classification. Among many methods for displaying data structure there is an interest in self organizing maps and its modifications. Neural gas network has shown surprisingly good results when capturing the inner structure of data. Therefore we propose its modification (correlation - based neural gas) and we use this network to visualize correlations between features. We discuss the posibility to use this additional information for fully automated unsupervised feature selection where no classification is available. The algorithm is tested on the EEG data acquired during the mental rotation task. (en)
Title
  • Correlation-based Neural Gas for Visualizing Correlations between EEG Features
  • Correlation-based Neural Gas for Visualizing Correlations between EEG Features (en)
skos:prefLabel
  • Correlation-based Neural Gas for Visualizing Correlations between EEG Features
  • Correlation-based Neural Gas for Visualizing Correlations between EEG Features (en)
skos:notation
  • RIV/68407700:21230/13:00194840!RIV14-MSM-21230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S, Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
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http://linked.open...dnocenehoVysledku
  • 67149
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/13:00194840
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Unsupervised Learning; Neural Networks; EEG; Mental Rotation; Feature Selection; Visualization (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [AE2FF537FE32]
http://linked.open...v/mistoKonaniAkce
  • Ostrava
http://linked.open...i/riv/mistoVydani
  • Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Advances in Intelligent Systems and Computing
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
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http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Lhotská, Lenka
  • Štěpánová, Karla
  • Macaš, Martin
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000312969500045
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
issn
  • 2194-5357
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-642-33018-6_45
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-642-33017-9
http://localhost/t...ganizacniJednotka
  • 21230
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