Differential entropy feature for EEG-based vigilance estimation

• Published in: 2013 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
• Language: English
• 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
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/EMBC.2013.6611075

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.