Document Detail


Comparison of filtering and classification techniques of electroencephalography for brain-computer interface.
MedLine Citation:
PMID:  19163244     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In this paper several methods are investigated for feature extraction and classification of mu features from electroencephalographic (EEG) readings of subjects engaged in motor tasks. EEG features are extracted by autoregressive (AR) filtering, mu-matched filtering, and wavelet decomposition (WD) methods, and the resulting features are classified by a linear classifier whose weights are set by an expert using a-priori knowledge, as well as support vector machines (SVM) using various kernels. The classification accuracies are compared to each other. SVMs are shown to offer a potential improvement over the simple linear classifier, and wavelets and mu-matched filtering are shown to offer potential improvement over AR filtering.
Authors:
Mark Renfrew; Roger Cheng; Janis J Daly; M Cavusoglu
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference     Volume:  2008     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2008  
Date Detail:
Created Date:  2009-02-16     Completed Date:  2009-04-28     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2634-7     Citation Subset:  IM    
Affiliation:
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA. (mark.renfrew@case.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Brain / physiology*
Cognition / physiology*
Electroencephalography / methods*
Evoked Potentials / physiology
Humans
Models, Theoretical
Neural Networks (Computer)
Pattern Recognition, Automated / methods*
Psychomotor Performance / physiology
Regression Analysis
Reproducibility of Results
Sensitivity and Specificity
User-Computer Interface

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


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