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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
1초록
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
- 발행일
- 2022-10
- 유형
- Conference paper
- 저널명
- 2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022
- 언어
- ENG
- 출판사
- Institute of Electrical and Electronics Engineers Inc.
- 발행국가
- 대한민국
- ISSN
- P 0000-0000