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

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.