Multi-Scale Sample Entropy as a Feature for Working Memory Study

被引：0
|
作者：
Angsuwatanakul, Thanate [1 ]
Iramina, Keiji [1 ]
Kaewkamnerdpong, Boonserm [2 ]
机构：
[1] Kyushu Univ, Grad Sch Syst Life Sci, Fukuoka, Japan
[2] KMUTT, Fac Engn, Biol Engn Dept, Bangkok, Thailand
来源：
2014 7TH BIOMEDICAL ENGINEERING INTERNATIONAL CONFERENCE (BMEICON) | 2014年
关键词：
neuroimaging; neuroinformatics; electroencephalography (EEG); multi-scale sample entropy (MSE); working memory; EEG COMPLEXITY; SPECTRUM; SLEEP;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
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.
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页数：5
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