Entropy is a powerful tool for the analysis of time series, as it allows describing the
probability distributions of the possible state of a system, and therefore the information …

Inspiration for artificial biologically inspired computing is often drawn from neural systems.
This article shows how to analyze neural systems using information theory with the aim of …

The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm.
However, the application of DL techniques in attention-deficit/hyperactivity disorder (ADHD) …

We introduce here phase transfer entropy (Phase TE) as a measure of directed connectivity
among neuronal oscillations. Phase TE quantifies the transfer entropy between phase time …

Abstract Information transfer is a key component of information processing, next to
information storage and modification. Information transfer can be measured by a variety of …

In this paper, we propose to use permutation entropy to explore whether the changes in
electroencephalogram (EEG) data can effectively distinguish different phases in human …

Objective In this study we develop a new complexity measure of time series by combining
ordinal patterns and Lempel-Ziv complexity (LZC) for quantifying the dynamical changes of …

Aim Deep brain stimulation (DBS) is a potential neuromodulatory therapy that enhances
recovery from disorders of consciousness, especially minimally conscious state (MCS). This …

Ordinal symbolic analysis opens an interesting and powerful perspective on time-series
analysis. Here, we review this relatively new approach and highlight its relation to symbolic …

Complexity measures are essential to understand complex systems and there are numerous
definitions to analyze one-dimensional data. However, extensions of these approaches to …