EEG feature extraction and analysis under drowsy state based on energy and sample entropy

In order to explore the effect of drowsiness on Electroencephalogram (EEG), EEG signals with bipolar lead C4-P4 are collected from 15 healthy subjects. There are six energy features and six sample entropy features of EEG signals under conscious and drowsy states extracted respectively. The study res...

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Bibliographic Details
Published in2012 5th International Conference on Biomedical Engineering and Informatics pp. 501 - 505
Main Authors Aihua Zhang, Yanfeng Chen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2012
Subjects
EEG signals
energy
feature extraction
sample entropy
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Summary:In order to explore the effect of drowsiness on Electroencephalogram (EEG), EEG signals with bipolar lead C4-P4 are collected from 15 healthy subjects. There are six energy features and six sample entropy features of EEG signals under conscious and drowsy states extracted respectively. The study results show that the energy under drowsy state increase obviously while the sample entropy under drowsy state decrease obviously compare with those under conscious state (p<;0.05), it offers a new method for drowsiness detection based on EEG.
ISBN:9781467311830
1467311839
DOI:10.1109/BMEI.2012.6513081