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多尺度熵作为脑电图和功能近红外光谱分析的新特征

Multiscale Entropy as a New Feature for EEG and fNIRS Analysis.

作者信息

Angsuwatanakul Thanate, O'Reilly Jamie, Ounjai Kajornvut, Kaewkamnerdpong Boonserm, Iramina Keiji

机构信息

Graduate School of Systems Life Sciences, Kyushu University, Fukuoka 819-0395, Japan.

College of Biomedical Engineering, Rangsit University, Pathum Thani 12000, Thailand.

出版信息

Entropy (Basel). 2020 Feb 7;22(2):189. doi: 10.3390/e22020189.

DOI:10.3390/e22020189
PMID:33285964
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7516618/
Abstract

The present study aims to apply multiscale entropy (MSE) to analyse brain activity in terms of brain complexity levels and to use simultaneous electroencephalogram and functional near-infrared spectroscopy (EEG/fNIRS) recordings for brain functional analysis. A memory task was selected to demonstrate the potential of this multimodality approach since memory is a highly complex neurocognitive process, and the mechanisms governing selective retention of memories are not fully understood by other approaches. In this study, 15 healthy participants with normal colour vision participated in the visual memory task, which involved the making the executive decision of remembering or forgetting the visual stimuli based on his/her own will. In a continuous stimulus set, 250 indoor/outdoor scenes were presented at random, between periods of fixation on a black background. The participants were instructed to make a binary choice indicating whether they wished to remember or forget the image; both stimulus and response times were stored for analysis. The participants then performed a scene recognition test to confirm whether or not they remembered the images. The results revealed that the participants intentionally memorising a visual scene demonstrate significantly greater brain complexity levels in the prefrontal and frontal lobe than when purposefully forgetting a scene; < 0.05 (two-tailed). This suggests that simultaneous EEG and fNIRS can be used for brain functional analysis, and MSE might be the potential indicator for this multimodality approach.

摘要

本研究旨在应用多尺度熵(MSE)从大脑复杂程度层面分析大脑活动,并使用同步脑电图和功能近红外光谱(EEG/fNIRS)记录进行大脑功能分析。选择了一项记忆任务来展示这种多模态方法的潜力,因为记忆是一个高度复杂的神经认知过程,而其他方法尚未完全理解控制记忆选择性保留的机制。在本研究中,15名具有正常色觉的健康参与者参与了视觉记忆任务,该任务涉及根据其自身意愿做出记住或忘记视觉刺激的执行决策。在连续的刺激组中,在固定于黑色背景的时间段之间随机呈现250个室内/室外场景。参与者被要求做出二元选择,表明他们是希望记住还是忘记图像;刺激和反应时间均被存储以供分析。参与者随后进行了场景识别测试,以确认他们是否记住了这些图像。结果显示,与有意忘记一个场景相比,有意记住一个视觉场景的参与者在前额叶和额叶表现出显著更高的大脑复杂程度水平;P < 0.05(双侧)。这表明同步脑电图和功能近红外光谱可用于大脑功能分析,而多尺度熵可能是这种多模态方法的潜在指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ab/7516618/e3cdf31d9e90/entropy-22-00189-g014.jpg
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