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Description
  • Electrical brain activity in subjects controlling Brain-Computer Interface (BCI) based on motor imagery is studied. A used data set contains 7440 observations corresponding to distributions of electrical potential at the head surface obtained by Independent Component Analysis of 155 48-channel EEG recordings over 16 subjects. The distributions are interpreted as produced by the current dipolar sources inside the head. To reveal the sources of electrical brain activity the most typical for motor imagery, the corresponding ICA components were clustered by Attractor Neural Network with Increasing Activity (ANNIA). ANNIA was already successfully applied to clustering textual documents and genome data. Among the expected clusters of components (blinks and mu-rhythm ERD) the ones reflecting the frontal and occipital cortex activity were also extracted. Although the cluster analysis can not substitute careful data examination and interpretation however it is a useful pre-processing step which can clearly aid in revealing data regularities which are impossible to tract by sequentially browsing through the data.
  • Electrical brain activity in subjects controlling Brain-Computer Interface (BCI) based on motor imagery is studied. A used data set contains 7440 observations corresponding to distributions of electrical potential at the head surface obtained by Independent Component Analysis of 155 48-channel EEG recordings over 16 subjects. The distributions are interpreted as produced by the current dipolar sources inside the head. To reveal the sources of electrical brain activity the most typical for motor imagery, the corresponding ICA components were clustered by Attractor Neural Network with Increasing Activity (ANNIA). ANNIA was already successfully applied to clustering textual documents and genome data. Among the expected clusters of components (blinks and mu-rhythm ERD) the ones reflecting the frontal and occipital cortex activity were also extracted. Although the cluster analysis can not substitute careful data examination and interpretation however it is a useful pre-processing step which can clearly aid in revealing data regularities which are impossible to tract by sequentially browsing through the data. (en)
Title
  • Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA)
  • Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA) (en)
skos:prefLabel
  • Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA)
  • Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA) (en)
skos:notation
  • RIV/61989100:27740/14:86092436!RIV15-MSM-27740___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070), P(EE.2.3.20.0073)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 7598
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  • RIV/61989100:27740/14:86092436
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • attractor neural network with increasing activity; independent component analysis; motor imagery; brain computer interface (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [CE2EA6EEA9F4]
http://linked.open...v/mistoKonaniAkce
  • Ostrava
http://linked.open...i/riv/mistoVydani
  • Basel
http://linked.open...i/riv/nazevZdroje
  • Advances in Intelligent Systems and Computing. Volume 237
http://linked.open...in/vavai/riv/obor
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Húsek, Dušan
  • Snášel, Václav
  • Frolov, Alexander
  • Bobrov, Pavel Dmitrievitch
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 2194-5357
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-319-08156-4_19
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-319-01780-8
http://localhost/t...ganizacniJednotka
  • 27740
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