Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task.
Lately, Ensemble Empirical Mode Decomposition (EEMD) techniques receive growing interest in biomedical data analysis. Event-Related Modes (ERMs) represent features extracted by an EEMD from electroencephalographic (EEG) recordings. We present a new approach for source localization of EEG data based...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2016-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC5148586?pdf=render |
id |
doaj-64566607f9e742cd8c7d2243d687335b |
---|---|
record_format |
Article |
spelling |
doaj-64566607f9e742cd8c7d2243d687335b2020-11-24T21:14:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-011112e016795710.1371/journal.pone.0167957Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task.Karema Al-SubariSaad Al-BaddaiAna Maria ToméGregor VolbergBernd LudwigElmar W LangLately, Ensemble Empirical Mode Decomposition (EEMD) techniques receive growing interest in biomedical data analysis. Event-Related Modes (ERMs) represent features extracted by an EEMD from electroencephalographic (EEG) recordings. We present a new approach for source localization of EEG data based on combining ERMs with inverse models. As the first step, 64 channel EEG recordings are pooled according to six brain areas and decomposed, by applying an EEMD, into their underlying ERMs. Then, based upon the problem at hand, the most closely related ERM, in terms of frequency and amplitude, is combined with inverse modeling techniques for source localization. More specifically, the standardized low resolution brain electromagnetic tomography (sLORETA) procedure is employed in this work. Accuracy and robustness of the results indicate that this approach deems highly promising in source localization techniques for EEG data.http://europepmc.org/articles/PMC5148586?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Karema Al-Subari Saad Al-Baddai Ana Maria Tomé Gregor Volberg Bernd Ludwig Elmar W Lang |
spellingShingle |
Karema Al-Subari Saad Al-Baddai Ana Maria Tomé Gregor Volberg Bernd Ludwig Elmar W Lang Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task. PLoS ONE |
author_facet |
Karema Al-Subari Saad Al-Baddai Ana Maria Tomé Gregor Volberg Bernd Ludwig Elmar W Lang |
author_sort |
Karema Al-Subari |
title |
Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task. |
title_short |
Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task. |
title_full |
Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task. |
title_fullStr |
Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task. |
title_full_unstemmed |
Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task. |
title_sort |
combined emd-sloreta analysis of eeg data collected during a contour integration task. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2016-01-01 |
description |
Lately, Ensemble Empirical Mode Decomposition (EEMD) techniques receive growing interest in biomedical data analysis. Event-Related Modes (ERMs) represent features extracted by an EEMD from electroencephalographic (EEG) recordings. We present a new approach for source localization of EEG data based on combining ERMs with inverse models. As the first step, 64 channel EEG recordings are pooled according to six brain areas and decomposed, by applying an EEMD, into their underlying ERMs. Then, based upon the problem at hand, the most closely related ERM, in terms of frequency and amplitude, is combined with inverse modeling techniques for source localization. More specifically, the standardized low resolution brain electromagnetic tomography (sLORETA) procedure is employed in this work. Accuracy and robustness of the results indicate that this approach deems highly promising in source localization techniques for EEG data. |
url |
http://europepmc.org/articles/PMC5148586?pdf=render |
work_keys_str_mv |
AT karemaalsubari combinedemdsloretaanalysisofeegdatacollectedduringacontourintegrationtask AT saadalbaddai combinedemdsloretaanalysisofeegdatacollectedduringacontourintegrationtask AT anamariatome combinedemdsloretaanalysisofeegdatacollectedduringacontourintegrationtask AT gregorvolberg combinedemdsloretaanalysisofeegdatacollectedduringacontourintegrationtask AT berndludwig combinedemdsloretaanalysisofeegdatacollectedduringacontourintegrationtask AT elmarwlang combinedemdsloretaanalysisofeegdatacollectedduringacontourintegrationtask |
_version_ |
1716747686126813184 |