Version 1
: Received: 17 February 2017 / Approved: 17 February 2017 / Online: 17 February 2017 (16:41:32 CET)
How to cite:
Keller, K.; Mangold, T.; Stolz, I.; Werner, J. Permutation Entropy: New Ideas and Challenges. Preprints.org2017, 2017020071. https://doi.org/10.20944/preprints201702.0071.v1
Keller, K.; Mangold, T.; Stolz, I.; Werner, J. Permutation Entropy: New Ideas and Challenges. Preprints.org 2017, 2017020071. https://doi.org/10.20944/preprints201702.0071.v1
Cite as:
Keller, K.; Mangold, T.; Stolz, I.; Werner, J. Permutation Entropy: New Ideas and Challenges. Preprints.org2017, 2017020071. https://doi.org/10.20944/preprints201702.0071.v1
Keller, K.; Mangold, T.; Stolz, I.; Werner, J. Permutation Entropy: New Ideas and Challenges. Preprints.org 2017, 2017020071. https://doi.org/10.20944/preprints201702.0071.v1
Abstract
During the last years some new variants of Permutation entropy have been introduced and applied to EEG analysis, among them a conditional variant and variants using some additional metric information or being based on entropies different from the Shannon entropy. In some situations it is not completely clear what kind of information the new measures and their algorithmic implementations provide. We discuss the new developments and illustrate them for EEG data.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
