Combining complexity measures of EEG data: multiplying measures reveal previously hidden information
- Published
- Accepted
- Subject Areas
- Computational Biology, Mathematical Biology, Neuroscience
- Keywords
- complexity, electroencephalograph, EEG, complexity measure, permutation entropy, fractal dimension, Higuchi complexity, sample entropy, algorithmic complexity, spectral flatness, Lemel-Ziv complexity, Weiner entropy, spectral structure index, spectral structure variability, information theory, chaos theory
- Copyright
- © 2015 Burns
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Combining complexity measures of EEG data: multiplying measures reveal previously hidden information. PeerJ PrePrints 3:e1121v1 https://doi.org/10.7287/peerj.preprints.1121v1
Abstract
Many studies have noted significant differences among human EEG results when participants or patients are exposed to different stimuli, undertaking different tasks, or being affected by conditions such as epilepsy or Alzheimer's disease. Such studies often use only one or two measures of complexity and do not regularly justify their choice of measure beyond the fact that it has been used in previous studies. If more measures were added to such studies, however, more complete information might be found about these reported differences. Such information might be useful in confirming the existence or extent of such differences, or in understanding their physiological bases. In this study I analysed publically-available EEG data using a range of complexity measures to determine how well the measures correlated with one another. The complexity measures did not all significantly correlate, suggesting that different measures were measuring unique features of the EEG signals and thus revealing information which other measures were unable to detect. Therefore, the results from this analysis suggests that combinations of complexity measures reveal unique information which is in addition to the information captured by other measures of complexity in EEG data. For this reason, researchers using individual complexity measures for EEG data should consider using combinations of measures to more completely account for any differences they observe and to ensure the robustness of any relationships identified.
Author Comment
This is a short study of common one-dimensional complexity measures applied to EEG recordings.
Supplemental Information
Data spreadsheet 1. Calculated complexity measures for 1100 EEG recordings
The following data are the results from MATLAB functions which calculated complexity measures for each EEG recording. ID = identification code as per Begleiter (1996); LZ = Lempel-Ziv algorithmic complexity; FD = fractal dimension estimate (Higuchi method); PE = permutation entropy; WE = Wiener entropy (also known as spectral flatness)