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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F12%3A00193379%21RIV13-MSM-21230___
rdf:type
skos:Concept n10:Vysledek
dcterms:description
This paper presents results of person identification experiments, based on individual properties of EEG signals. EEG data of each test subject were acquired in two different measurement sessions, with one year time gap between them. Main focus of these experiments was to assess long-term temporal stability of EEG biometrics. Use of EEG data from separate session leads to identification score about 98%. Results for merged EEG sessions reach in average only 78% success rate. Identification algorithm used for these experiments utilizes Frequency-Zooming Auto-Regression modeling and Mahalanobis distance-based classifier, several improvements for this algorithm are proposed in this paper. This paper presents results of person identification experiments, based on individual properties of EEG signals. EEG data of each test subject were acquired in two different measurement sessions, with one year time gap between them. Main focus of these experiments was to assess long-term temporal stability of EEG biometrics. Use of EEG data from separate session leads to identification score about 98%. Results for merged EEG sessions reach in average only 78% success rate. Identification algorithm used for these experiments utilizes Frequency-Zooming Auto-Regression modeling and Mahalanobis distance-based classifier, several improvements for this algorithm are proposed in this paper.
dcterms:title
EEG signal as biometric characteristic and its long-term temporal stability EEG signal as biometric characteristic and its long-term temporal stability
skos:prefLabel
EEG signal as biometric characteristic and its long-term temporal stability EEG signal as biometric characteristic and its long-term temporal stability
skos:notation
RIV/68407700:21230/12:00193379!RIV13-MSM-21230___
n10:predkladatel
n11:orjk%3A21230
n4:aktivita
n13:S
n4:aktivity
S
n4:dodaniDat
n21:2013
n4:domaciTvurceVysledku
n19:3900053
n4:druhVysledku
n17:D
n4:duvernostUdaju
n7:S
n4:entitaPredkladatele
n12:predkladatel
n4:idSjednocenehoVysledku
132855
n4:idVysledku
RIV/68407700:21230/12:00193379
n4:jazykVysledku
n14:eng
n4:klicovaSlova
electroencephalography; biometrics; person identification; frequency-zooming auto-regression modeling
n4:klicoveSlovo
n5:person%20identification n5:biometrics n5:electroencephalography n5:frequency-zooming%20auto-regression%20modeling
n4:kontrolniKodProRIV
[61684CE6C83A]
n4:mistoKonaniAkce
Prague
n4:mistoVydani
Praha
n4:nazevZdroje
POSTER 2012 - 16th International Student Conference on Electrical Engineering
n4:obor
n20:JA
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
1
n4:rokUplatneniVysledku
n21:2012
n4:tvurceVysledku
Kostílek, Milan
n4:typAkce
n9:EUR
n4:zahajeniAkce
2012-05-17+02:00
s:numberOfPages
4
n15:hasPublisher
České vysoké učení technické v Praze
n6:isbn
978-80-01-05043-9
n18:organizacniJednotka
21230