Differential entropy was the most accurate and stable EEG feature to reflect vigilance changes when compared to four existing features in a dataset of 23 subjects.
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.
Shi et al. (Mon,) conducted a other in Vigilance estimation (n=23). Differential entropy feature vs. Four existing features was evaluated on Accuracy and stability of vigilance estimation. Differential entropy was the most accurate and stable EEG feature to reflect vigilance changes when compared to four existing features in a dataset of 23 subjects.