Document Detail

Information quantification of empirical mode decomposition and applications to field potentials.
MedLine Citation:
PMID:  21243730     Owner:  NLM     Status:  In-Data-Review    
The empirical mode decomposition (EMD) method can adaptively decompose a non-stationary time series into a number of amplitude or frequency modulated functions known as intrinsic mode functions. This paper combines the EMD method with information analysis and presents a framework of information-preserving EMD. The enhanced EMD method has been exploited in the analysis of neural recordings. It decomposes a signal and extracts only the most informative oscillations contained in the non-stationary signal. Information analysis has shown that the extracted components retain the information content of the signal. More importantly, a limited number of components reveal the main oscillations presented in the signal and their instantaneous frequencies, which are not often obvious from the original signal. This information-coupled EMD method has been tested on several field potential datasets for the analysis of stimulus coding in visual cortex, from single and multiple channels, and for finding information connectivity among channels. The results demonstrate the usefulness of the method in extracting relevant responses from the recorded signals. An investigation is also conducted on utilizing the Hilbert phase for cases where phase information can further improve information analysis and stimulus discrimination. The components of the proposed method have been integrated into a toolbox and the initial implementation is also described.
Zareen Mehboob; Hujun Yin
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  International journal of neural systems     Volume:  21     ISSN:  0129-0657     ISO Abbreviation:  Int J Neural Syst     Publication Date:  2011 Feb 
Date Detail:
Created Date:  2011-01-18     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9100527     Medline TA:  Int J Neural Syst     Country:  Singapore    
Other Details:
Languages:  eng     Pagination:  49-63     Citation Subset:  IM    
School of Electrical and Electronic Engineering, The University of Manchester, Manchester, M60 1QD, UK.
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