Multi-scale sample entropy as a feature for working memory study
Multi-scale sample entropy as a feature for working memory study
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多尺度样本熵作为工作记忆研究的特征
DOI：
10.1109/bmeicon.2014.7017446
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Boonserm Kaewkamnerdpong
中科院分区：
文献类型：
--
作者：
Thanate Angsuwatanakul;Keiji Iramina;Boonserm Kaewkamnerdpong
为了了解人类大脑是如何工作的，以便我们能够设法有效地改善神经系统疾病的状况，甚至提高认知能力，工作记忆研究是令人感兴趣的。多尺度样本熵已被用于分析生物医学数据的复杂性。本研究旨在探讨使用多尺度样本熵作为表征记忆的特征的潜力。我们应用复杂性分析的EEG数据记录在认知实验中，通过视觉刺激针对工作记忆。结果显示，不同记忆情况下，前额区的样本熵有明显差异。这表明使用多尺度样本熵表征记忆的潜力。
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.