Document Detail


Selection of a Subset of EEG Channels using PCA to classify Alcoholics and Non-alcoholics.
MedLine Citation:
PMID:  17281159     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
The Principal Component Analysis (PCA) is proposed as feature selection method in choosing a subset of channels for Visual Evoked Potentials (VEP). The selected channels are to preserve as much information present as compared to the full set of 61 channels as possible. The method is applied to classify two categories of subjects: alcoholics and non-alcoholics. The electroencephalogram (EEG) was recorded when the subjects were presented with single trial visual stimuli. The proposed method is successful in selecting the a subset of channels that contribute to high accuracy in the classification of alcoholics and non-alcoholics.
Authors:
Kok-Meng Ong; K-H Thung; Chong-Yaw Wee; Raveendran Paramesran
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  4     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2005  
Date Detail:
Created Date:  2007-02-06     Completed Date:  2012-10-02     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  4195-8     Citation Subset:  -    
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