Multi-scale sample entropy as a feature for working memory study

Thanate Angsuwatanakul, Keiji Iramina, Boonserm Kaewkamnerdpong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Toward the understanding of how human brains work so that we could manage to effectively improve the conditions of neurological disorders or even enhance the cognitive performance, working memory study is of interest. Multi-scale sample entropy has been used to analyze the complexity of biomedical data. This study aims to investigate the potential of using multi-scale sample entropy as a feature for characterizing memory. We applied complexity analysis on EEG data recorded during a cognitive experiment targeting working memory through visual stimuli. The results revealed the distinctive sample entropy for various memory cases in prefrontal area. This indicated the potential of using multi-scale sample entropy for characterizing memory.

Original languageEnglish
Title of host publicationBMEiCON 2014 - 7th Biomedical Engineering International Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479968015
DOIs
Publication statusPublished - Jan 20 2015
Event7th Biomedical Engineering International Conference, BMEiCON 2014 - Fukuoka, Japan
Duration: Nov 26 2014Nov 28 2014

Publication series

NameBMEiCON 2014 - 7th Biomedical Engineering International Conference

Other

Other7th Biomedical Engineering International Conference, BMEiCON 2014
CountryJapan
CityFukuoka
Period11/26/1411/28/14

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Multi-scale sample entropy as a feature for working memory study'. Together they form a unique fingerprint.

Cite this