Overcoming Inter-Subject Variability in BCI Using EEG-Based Identification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00217616" target="_blank" >RIV/68407700:21230/14:00217616 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Overcoming Inter-Subject Variability in BCI Using EEG-Based Identification

Popis výsledku v původním jazyce

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 Zoo

Název v anglickém jazyce

Overcoming Inter-Subject Variability in BCI Using EEG-Based Identification

Popis výsledku anglicky

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 Zoo

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Radioengineering

ISSN

1210-2512

e-ISSN

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Svazek periodika

23

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

266-273

Kód UT WoS článku

000334729400032

EID výsledku v databázi Scopus

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