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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00217616%21RIV15-MSM-21230___
rdf:type
n6:Vysledek skos:Concept
dcterms:description
The high dependency of the Brain Computer Interface (BCI) system performance on the BCI user is a well-known issue of many BCI devices. This contribution presents a new way to overcome this problem using a synergy between a BCI device and an EEG-based biometric algorithm. Using the biometric algorithm, the BCI device automatically identifies its current user and adapts parameters of the classification process and of the BCI protocol to maximize the BCI performance. In addition to this we present an algorithm for EEG-based identification designed to be resistant to variations in EEG recordings between sessions, which is also demonstrated by an experiment with an EEG database containing two sessions recorded one year apart. Further, our algorithm is designed to be compatible with our movement-related BCI device and the evaluation of the algorithm performance took place under conditions of a standard BCI experiment. Estimation of the mu rhythm fundamental frequency using the Frequency Zooming AR modeling is used for EEG feature extraction followed by a classifier based on the regularized Mahalanobis distance. An average subject identification score of 96 % is achieved. The high dependency of the Brain Computer Interface (BCI) system performance on the BCI user is a well-known issue of many BCI devices. This contribution presents a new way to overcome this problem using a synergy between a BCI device and an EEG-based biometric algorithm. Using the biometric algorithm, the BCI device automatically identifies its current user and adapts parameters of the classification process and of the BCI protocol to maximize the BCI performance. In addition to this we present an algorithm for EEG-based identification designed to be resistant to variations in EEG recordings between sessions, which is also demonstrated by an experiment with an EEG database containing two sessions recorded one year apart. Further, our algorithm is designed to be compatible with our movement-related BCI device and the evaluation of the algorithm performance took place under conditions of a standard BCI experiment. Estimation of the mu rhythm fundamental frequency using the Frequency Zooming AR modeling is used for EEG feature extraction followed by a classifier based on the regularized Mahalanobis distance. An average subject identification score of 96 % is achieved.
dcterms:title
Overcoming Inter-Subject Variability in BCI Using EEG-Based Identification Overcoming Inter-Subject Variability in BCI Using EEG-Based Identification
skos:prefLabel
Overcoming Inter-Subject Variability in BCI Using EEG-Based Identification Overcoming Inter-Subject Variability in BCI Using EEG-Based Identification
skos:notation
RIV/68407700:21230/14:00217616!RIV15-MSM-21230___
n3:aktivita
n14:S
n3:aktivity
S
n3:cisloPeriodika
1
n3:dodaniDat
n17:2015
n3:domaciTvurceVysledku
n4:3013472 n4:3900053 n4:4497783
n3:druhVysledku
n15:J
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
35388
n3:idVysledku
RIV/68407700:21230/14:00217616
n3:jazykVysledku
n12:eng
n3:klicovaSlova
bci
n3:klicoveSlovo
n11:bci
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[182FC4DCCD17]
n3:nazevZdroje
Radioengineering
n3:obor
n9:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n17:2014
n3:svazekPeriodika
23
n3:tvurceVysledku
Šťastný, Jakub Sovka, Pavel Kostílek, Milan
n3:wos
000334729400032
s:issn
1210-2512
s:numberOfPages
8
n5:organizacniJednotka
21230