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Description
  • Práce se zabývá optimalizací klasiifkace osob pomocí EEG signálu. Předchozí výsledky ukazují, že je možné provádět identifikaci osob na základě jejich mí a alfa rytmu. Tato práce je zaměřena na objasnění našich předchozích výsledků. Ukazujeme, že problémy při klasifikaci jsou způsobené specifickým charakterem EEG a že je možné frekvenční rozlilšení snížit až na 50% původní hodnoty. (cs)
  • This work deals with an optimization of the EEG-based biometric classification. Recent results and other published works indicate that it is possible to identify subjects on the base of their alpha and u rhythms spectral envelopes. Our latest work was targeted to the explanation of some past results and to the optimization of unfeasibly high frequency resolution used previously. The results showed that the problems with identification of some subjects have roots in the EEG signal itself. Further, it is shown that the frequency resolution might be reduced by 50% (from 1/180Hz to 1/90Hz) reducing the required segment length to identify subject from 180 sec to 90 sec.
  • This work deals with an optimization of the EEG-based biometric classification. Recent results and other published works indicate that it is possible to identify subjects on the base of their alpha and u rhythms spectral envelopes. Our latest work was targeted to the explanation of some past results and to the optimization of unfeasibly high frequency resolution used previously. The results showed that the problems with identification of some subjects have roots in the EEG signal itself. Further, it is shown that the frequency resolution might be reduced by 50% (from 1/180Hz to 1/90Hz) reducing the required segment length to identify subject from 180 sec to 90 sec. (en)
Title
  • The optimization of the EEG-based biometric classification
  • Optimalizace biometrické klasifikace pomocí EEG signálu (cs)
  • The optimization of the EEG-based biometric classification (en)
skos:prefLabel
  • The optimization of the EEG-based biometric classification
  • Optimalizace biometrické klasifikace pomocí EEG signálu (cs)
  • The optimization of the EEG-based biometric classification (en)
skos:notation
  • RIV/68407700:21230/07:03132668!RIV08-GA0-21230___
http://linked.open.../vavai/riv/strany
  • 25;28
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GD102/03/H085), Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 439904
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/07:03132668
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • EEG (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [2571E8470B91]
http://linked.open...v/mistoKonaniAkce
  • Pilsen
http://linked.open...i/riv/mistoVydani
  • Plzeň
http://linked.open...i/riv/nazevZdroje
  • Applied Electronics 2007
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Šťastný, Jakub
  • Cempírek, M.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Západočeská univerzita v Plzni
https://schema.org/isbn
  • 978-80-7043-537-3
http://localhost/t...ganizacniJednotka
  • 21230
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