Single-Trial Classification of Elementary Comparison Processes on the Basis of Instantaneous EEG and MEG Coherences

The present study is focused on the evidence of possible single-trial EEG/MEG analysis of information processing. The discrimination between thinking modalities of concept activation and pattern comparison for single tasks of elementary comparison procedures is investigated. A neural network classif...

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Published in:Brain topography Vol. 15; no. 2; pp. 125 - 137
Main Authors: Steuer, Dunja, Grieszbach, Gert, Krause, Werner, Schack, Bärbel
Format: Journal Article
Language:English
Published: United States Springer Nature B.V 01.12.2002
Subjects:
Adult
Algorithms
Automatic Data Processing - methods
Classification - methods
Cognition
Concept Formation
Discriminant Analysis
Electroencephalography
Functional Laterality
Humans
Magnetoencephalography
Models, Neurological
Neural Networks (Computer)
Pattern Recognition, Visual
Reaction Time
Signal Processing, Computer-Assisted
ISSN:0896-0267, 1573-6792
Online Access:Get full text
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Abstract The present study is focused on the evidence of possible single-trial EEG/MEG analysis of information processing. The discrimination between thinking modalities of concept activation and pattern comparison for single tasks of elementary comparison procedures is investigated. A neural network classifier with backpropagation learning algorithm is used. The input vector is constructed by parameters of instantaneous coherence (13-20 Hz) between several channel pairs of the EEG and/or of the MEG. Thereby, the strength of synchronization and the time location of synchronization phenomena are taken into consideration. The combination of EEG and MEG coherence parameters led to a classification accuracy of 85-94% for single subjects. Generally, results reached by neural network classifier show a better generalization than linear discriminant analysis.
AbstractList The present study is focused on the evidence of possible single-trial EEG/MEG analysis of information processing. The discrimination between thinking modalities of concept activation and pattern comparison for single tasks of elementary comparison procedures is investigated. A neural network classifier with backpropagation learning algorithm is used. The input vector is constructed by parameters of instantaneous coherence (13-20 Hz) between several channel pairs of the EEG and/or of the MEG. Thereby, the strength of synchronization and the time location of synchronization phenomena are taken into consideration. The combination of EEG and MEG coherence parameters led to a classification accuracy of 85-94% for single subjects. Generally, results reached by neural network classifier show a better generalization than linear discriminant analysis.
The present study is focused on the evidence of possible single-trial EEG/MEG analysis of information processing. The discrimination between thinking modalities of concept activation and pattern comparison for single tasks of elementary comparison procedures is investigated. A neural network classifier with backpropagation learning algorithm is used. The input vector is constructed by parameters of instantaneous coherence (13-20 Hz) between several channel pairs of the EEG and/or of the MEG. Thereby, the strength of synchronization and the time location of synchronization phenomena are taken into consideration. The combination of EEG and MEG coherence parameters led to a classification accuracy of 85-94% for single subjects. Generally, results reached by neural network classifier show a better generalization than linear discriminant analysis.The present study is focused on the evidence of possible single-trial EEG/MEG analysis of information processing. The discrimination between thinking modalities of concept activation and pattern comparison for single tasks of elementary comparison procedures is investigated. A neural network classifier with backpropagation learning algorithm is used. The input vector is constructed by parameters of instantaneous coherence (13-20 Hz) between several channel pairs of the EEG and/or of the MEG. Thereby, the strength of synchronization and the time location of synchronization phenomena are taken into consideration. The combination of EEG and MEG coherence parameters led to a classification accuracy of 85-94% for single subjects. Generally, results reached by neural network classifier show a better generalization than linear discriminant analysis.
Author Grieszbach, Gert
Krause, Werner
Schack, Bärbel
Steuer, Dunja
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  surname: Krause
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  givenname: Bärbel
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crossref_primary_10_1111_j_1469_8986_2009_00816_x
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SubjectTerms Adult
Algorithms
Automatic Data Processing - methods
Classification - methods
Cognition
Concept Formation
Discriminant Analysis
Electroencephalography
Functional Laterality
Humans
Magnetoencephalography
Models, Neurological
Neural Networks (Computer)
Pattern Recognition, Visual
Reaction Time
Signal Processing, Computer-Assisted
Title Single-Trial Classification of Elementary Comparison Processes on the Basis of Instantaneous EEG and MEG Coherences
URI https://www.ncbi.nlm.nih.gov/pubmed/12537308
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Volume 15
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