Information preserving empirical mode decomposition for filtering field potentials

Authors:
Zareen Mehboob;Hujun Yin
Affiliations:
The University of Manchester;The University of Manchester
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

Citing 3
Cited 1

Elements of information theory

Elements of information theory
Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention

Biological Cybernetics
Assessing Neuronal Coherence with Single-Unit, Multi-Unit, and Local Field Potentials

Neural Computation

Neural data analysis and reduction using improved framework of information-preserving EMD

IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning

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Abstract

This paper presents a concept of using the empirical mode decomposition (EMD) as a filtering tool to extract information-preserving intrinsic mode functions (IMFs) in an adaptive manner. The approach is tested on several local field potentials (LFPs) and information quantification is carried out in spectral domain using Shannon's Information. The study suggests that not all IMFs are information carriers. It is found that the 1st IMF carries 60-80% of the total information from original LFP and few informative IMFs are usually the main information carriers. Adding more IMFs does not increase the information level. For different datasets, the order of the informative IMFs varies and by using information preserving EMD, only few IMFs are retained to provide a simplified representation of underlying oscillations contained in LFPs.