EEG artifact elimination by extraction of ICA-component features using image processing algorithms

[Display omitted] •Machine-driven EEG artifact removal through automated selection of ICs is proposed.•Feature vectors extracted from IC via image processing algorithms are used.•LDA classification identifies range filter as powerful feature (accuracy rate 88%).•The method does not depend on direct...

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Published in:	Journal of neuroscience methods Vol. 243; pp. 84 - 93
Main Authors:	Radüntz, T., Scouten, J., Hochmuth, O., Meffert, B.
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 30.03.2015
Subjects:	Adult Algorithms Artifact elimination Brain - physiology Discriminant Analysis EEG Electroencephalography - methods Executive Function - physiology Female Geometric features Humans ICA Image processing Image Processing, Computer-Assisted - methods Independent component analysis Linear Models Local binary patterns Male Middle Aged Pattern Recognition, Automated - methods Range filter Signal Processing, Computer-Assisted Signal-To-Noise Ratio 68U99 68U10 68T10 Image processing ICA EEG Independent component analysis Artifact elimination 92C55 Geometric features Local binary patterns Range filter 68-04 92C50
ISSN:	0165-0270, 1872-678X, 1872-678X
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Abstract	[Display omitted] •Machine-driven EEG artifact removal through automated selection of ICs is proposed.•Feature vectors extracted from IC via image processing algorithms are used.•LDA classification identifies range filter as powerful feature (accuracy rate 88%).•The method does not depend on direct recording of artifact signals.•The method is not limited to a specific number of artifacts. Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes.
AbstractList	[Display omitted] •Machine-driven EEG artifact removal through automated selection of ICs is proposed.•Feature vectors extracted from IC via image processing algorithms are used.•LDA classification identifies range filter as powerful feature (accuracy rate 88%).•The method does not depend on direct recording of artifact signals.•The method is not limited to a specific number of artifacts. Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes. Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes. Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes.Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes.
Author	Radüntz, T. Meffert, B. Hochmuth, O. Scouten, J.
Author_xml	– sequence: 1 givenname: T. orcidid: 0000-0003-1882-2005 surname: Radüntz fullname: Radüntz, T. email: raduentz.thea@baua.bund.de organization: Federal Institute for Occupational Safety and Health, Mental Health and Cognitive Capacity, Nöldnerstr. 40-42, 10317 Berlin, Germany – sequence: 2 givenname: J. surname: Scouten fullname: Scouten, J. organization: Federal Institute for Occupational Safety and Health, Mental Health and Cognitive Capacity, Nöldnerstr. 40-42, 10317 Berlin, Germany – sequence: 3 givenname: O. surname: Hochmuth fullname: Hochmuth, O. organization: Humboldt-Universität zu Berlin, Department of Computer Science, Rudower Chaussee 25, 12489 Berlin, Germany – sequence: 4 givenname: B. surname: Meffert fullname: Meffert, B. organization: Humboldt-Universität zu Berlin, Department of Computer Science, Rudower Chaussee 25, 12489 Berlin, Germany
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Keywords	68U99 68U10 68T10 Image processing ICA EEG Independent component analysis Artifact elimination 92C55 Geometric features Local binary patterns Range filter 68-04 92C50
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Snippet	[Display omitted] •Machine-driven EEG artifact removal through automated selection of ICs is proposed.•Feature vectors extracted from IC via image processing... Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in...
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SubjectTerms	Adult Algorithms Artifact elimination Brain - physiology Discriminant Analysis EEG Electroencephalography - methods Executive Function - physiology Female Geometric features Humans ICA Image processing Image Processing, Computer-Assisted - methods Independent component analysis Linear Models Local binary patterns Male Middle Aged Pattern Recognition, Automated - methods Range filter Signal Processing, Computer-Assisted Signal-To-Noise Ratio
Title	EEG artifact elimination by extraction of ICA-component features using image processing algorithms
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