P3 Component Detection Using HHT Improvement of EMD with Additional Stopping Criteria

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F14%3A43923183" target="_blank" >RIV/49777513:23520/14:43923183 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-09891-3_10" target="_blank" >http://dx.doi.org/10.1007/978-3-319-09891-3_10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-09891-3_10" target="_blank" >10.1007/978-3-319-09891-3_10</a>

Jazyk výsledku

angličtina

Název v původním jazyce

P3 Component Detection Using HHT Improvement of EMD with Additional Stopping Criteria

Popis výsledku v původním jazyce

This paper describes improvement of the Hilbert-Huang transform (HHT) for detection of ERP components in the EEG signal. Time-frequency domain methods, such as the wavelet transform or matching pursuit, are commonly for this task. We used a modified Hilbert-Huang transform that allows the processing of quasi-stationary signals such as EEG. The essential part of the HHT is an Empirical Mode Decomposition (EMD) that decomposes signal into intrinsic mode functions (IMFs). We designed additional stopping criteria for better selection of IMFs in the EMD. These IMFs positively affect later computed instantaneous attributes and increase classification success. We tested the influence of additional stopping criteria on classification reliability using the realEEG data acquired in our laboratory. Our results demonstrated that we were able to detect the P3 component by using the HHT with additional stopping criteria more successfully than by using the original implementation of modified HHT, co

Název v anglickém jazyce

P3 Component Detection Using HHT Improvement of EMD with Additional Stopping Criteria

Popis výsledku anglicky

This paper describes improvement of the Hilbert-Huang transform (HHT) for detection of ERP components in the EEG signal. Time-frequency domain methods, such as the wavelet transform or matching pursuit, are commonly for this task. We used a modified Hilbert-Huang transform that allows the processing of quasi-stationary signals such as EEG. The essential part of the HHT is an Empirical Mode Decomposition (EMD) that decomposes signal into intrinsic mode functions (IMFs). We designed additional stopping criteria for better selection of IMFs in the EMD. These IMFs positively affect later computed instantaneous attributes and increase classification success. We tested the influence of additional stopping criteria on classification reliability using the realEEG data acquired in our laboratory. Our results demonstrated that we were able to detect the P3 component by using the HHT with additional stopping criteria more successfully than by using the original implementation of modified HHT, co

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

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 statě ve sborníku

Brain Informatics and Health

ISBN

978-3-319-09890-6

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

11

Strana od-do

100-110

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Warsaw

Datum konání akce

11. 8. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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