Document Type

Journal Article

Publication Title

Brain Sciences

Publisher

MDPI

School

School of Medical and Health Sciences

RAS ID

30029

Funders

The research was partially supported by CNPq, FAPESP and CAPES from Brazil. Research carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP (grant 2013/07375-0).

Comments

Originally published as: Nascimento, D. C., Depetri, G., Stefano, L. H., Anacleto, O., Leite, J. P., Edwards, D. J., ... & Louzada Neto, F. (2019). Entropy analysis of high-definition transcranial electric stimulation effects on EEG dynamics. Brain Sciences, 9(8).

Original article available here.

Abstract

A foundation of medical research is time series analysis—the behavior of variables of interest with respect to time. Time series data are often analyzed using the mean, with statistical tests applied to mean differences, and has the assumption that data are stationary. Although widely practiced, this method has limitations. Here we present an alternative statistical approach with sample analysis that provides a summary statistic accounting for the non-stationary nature of time series data. This work discusses the use of entropy as a measurement of the complexity of time series, in the context of Neuroscience, due to the non-stationary characteristic of the data. To elucidate our argument, we conducted entropy analysis on a sample of electroencephalographic (EEG) data from an interventional study using non-invasive electrical brain stimulation. We demonstrated that entropy analysis could identify intervention-related change in EEG data, supporting that entropy can be a useful “summary” statistic in non-linear dynamical systems.

DOI

10.3390/brainsci9080208

Access Rights

free_to_read

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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