Cognitive modeling using multivariate multiscale entropy analysis of EEG: entropy changes according to auditory inputs and the level of attention

  • Kim, Dong-Young; 
  • Heo, Jae-Wook; 
  • Kim, Young-Tak; 
  • Kim, Jung Bin; 
  • Kim, Dong-Joo
Citations

SCOPUS

1

초록

Multivariate multiscale entropy (MMSE) is one measure to measure the amount of information in a random signal. The amount of information contained in electroencephalogram (EEG) signals that vary depending on auditory input and state was measured using MMSE in this study. As a result, the MMSE value rises not only when rehearsal of a given sentence is performed, but also when a non-semantic sentence is given, and appropriate noise is mixed in with the input sentence. As a result of these findings, this study proposes a method for quantitatively analyzing various cognitive models. © 2022 IEEE.

키워드

EEG; Information processing; MMSE
제목
Cognitive modeling using multivariate multiscale entropy analysis of EEG: entropy changes according to auditory inputs and the level of attention
저자
Kim, Dong-Young; Heo, Jae-Wook; Kim, Young-Tak; Kim, Jung Bin; Kim, Dong-Joo
DOI
10.1109/ICCE-Asia57006.2022.9954710
발행일
2022-10
유형
Conference paper
저널명
2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022