Entropy Analysis of High-Definition Transcranial Electric Stimulation Effects on EEG Dynamics

Brain Sciences

DiegoC. Nascimento¹  Gabriela Depetri¹  Francisco Louzada Neto¹  Osvaldo Anacleto¹  DylanJ. Edwards²  TaizaE. G. Santos³  LuizH. Stefano³  JoaoP. Leite³ 

[1] Institute of Mathematical Science and Computing, University of Sao Paulo, Sao Carlos 13566-590, Brazil;Moss Rehabilitation Research Institute, Elkins Park, PA 19027, USA;Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto 14049-900, Brazil;

关键词: complexity measure;    approximated entropy;    time series;    biosignal;   

DOI  :  10.3390/brainsci9080208

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown