Klasifikace EEG s vysokým rozlišením

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03143057" target="_blank" >RIV/68407700:21230/08:03143057 - 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

High-Resolution Movement EEG Classification

Popis výsledku v původním jazyce

The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjectswas created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the resultsof other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94-100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.

Název v anglickém jazyce

High-Resolution Movement EEG Classification

Popis výsledku anglicky

The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjectswas created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the resultsof other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94-100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2008

Kód důvěrnosti údajů

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

Název periodika

Computational Intelligence and Neuroscience

ISSN

1687-5265

e-ISSN

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Svazek periodika

2008

Číslo periodika v rámci svazku

15.01.08

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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