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Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, t...

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Detalles Bibliográficos
Autores principales: Halder, Sebastian, Bensch, Michael, Mellinger, Jürgen, Bogdan, Martin, Kübler, Andrea, Birbaumer, Niels, Rosenstiel, Wolfgang
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2234090/
https://www.ncbi.nlm.nih.gov/pubmed/18288259
http://dx.doi.org/10.1155/2007/82069
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author Halder, Sebastian
Bensch, Michael
Mellinger, Jürgen
Bogdan, Martin
Kübler, Andrea
Birbaumer, Niels
Rosenstiel, Wolfgang
author_facet Halder, Sebastian
Bensch, Michael
Mellinger, Jürgen
Bogdan, Martin
Kübler, Andrea
Birbaumer, Niels
Rosenstiel, Wolfgang
author_sort Halder, Sebastian
collection PubMed
description We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.
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spelling pubmed-22340902008-02-20 Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation Halder, Sebastian Bensch, Michael Mellinger, Jürgen Bogdan, Martin Kübler, Andrea Birbaumer, Niels Rosenstiel, Wolfgang Comput Intell Neurosci Research Article We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method. Hindawi Publishing Corporation 2007 2007-11-13 /pmc/articles/PMC2234090/ /pubmed/18288259 http://dx.doi.org/10.1155/2007/82069 Text en Copyright © 2007 Sebastian Halder et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Halder, Sebastian
Bensch, Michael
Mellinger, Jürgen
Bogdan, Martin
Kübler, Andrea
Birbaumer, Niels
Rosenstiel, Wolfgang
Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation
title Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation
title_full Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation
title_fullStr Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation
title_full_unstemmed Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation
title_short Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation
title_sort online artifact removal for brain-computer interfaces using support vector machines and blind source separation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2234090/
https://www.ncbi.nlm.nih.gov/pubmed/18288259
http://dx.doi.org/10.1155/2007/82069
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