Document Detail

Enhancing the signal-to-noise ratio of ICA-based extracted ERPs.
MedLine Citation:
PMID:  16602566     Owner:  NLM     Status:  MEDLINE    
When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings.
Steven Lemm; Gabriel Curio; Yevhen Hlushchuk; Klaus-Robert Müller
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Publication Detail:
Type:  Clinical Trial; Comparative Study; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  53     ISSN:  0018-9294     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2006 Apr 
Date Detail:
Created Date:  2006-04-10     Completed Date:  2006-05-02     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  601-7     Citation Subset:  IM    
Department of Intelligent Data Analysis, FIRST Fraunhofer Institute, 12489 Berlin, Germany.
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MeSH Terms
Brain / physiology*
Computer Simulation
Diagnosis, Computer-Assisted / methods*
Electroencephalography / methods*
Evoked Potentials / physiology*
Models, Neurological*
Models, Statistical
Pattern Recognition, Automated / methods
Principal Component Analysis
Reproducibility of Results
Sensitivity and Specificity
Stochastic Processes

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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