Differential entropy feature for EEG-based vigilance estimation

This paper proposes a novel feature called differential entropy for EEG-based vigilance estimation. By mathematical derivation, we find an interesting relationship between the proposed differential entropy and the existing logarithm energy spectrum. We present a physical interpretation of the logari...

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Bibliographic Details
Published in2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2013; pp. 6627 - 6630
Main Authors Shi, Li-Chen, Jiao, Ying-Ying, Lu, Bao-Liang
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2013
Subjects
Adult
Algorithms
Arousal - physiology
Electroencephalography
Electroencephalography - methods
Entropy
Estimation
Feature extraction
Female
Fractals
Frequency estimation
Humans
Male
Problem Solving - physiology
Signal Processing, Computer-Assisted
Visualization
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Summary:This paper proposes a novel feature called differential entropy for EEG-based vigilance estimation. By mathematical derivation, we find an interesting relationship between the proposed differential entropy and the existing logarithm energy spectrum. We present a physical interpretation of the logarithm energy spectrum which is widely used in EEG signal analysis. To evaluate the performance of the proposed differential entropy feature for vigilance estimation, we compare it with four existing features on an EEG data set of twenty-three subjects. All of the features are projected to the same dimension by principal component analysis algorithm. Experiment results show that differential entropy is the most accurate and stable EEG feature to reflect the vigilance changes.
ISSN:1094-687X
1557-170X
DOI:10.1109/EMBC.2013.6611075