Suppr超能文献

使用脑电图(EEG)和功能近红外光谱(fNIRS)联合测量对动作观察进行神经活动与解码

Neural Activity and Decoding of Action Observation Using Combined EEG and fNIRS Measurement.

作者信息

Ge Sheng, Wang Peng, Liu Hui, Lin Pan, Gao Junfeng, Wang Ruimin, Iramina Keiji, Zhang Quan, Zheng Wenming

机构信息

Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.

Department of Psychology and Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Changsha, China.

出版信息

Front Hum Neurosci. 2019 Oct 15;13:357. doi: 10.3389/fnhum.2019.00357. eCollection 2019.

Abstract

In a social world, observing the actions of others is fundamental to understanding what they are doing, as well as their intentions and feelings. Studies of the neural basis and decoding of action observation are important for understanding action-related processes and have implications for cognitive, social neuroscience, and human-machine interaction (HMI). In the current study, we first investigated temporal-spatial dynamics during action observation using a combined 64-channel electroencephalography (EEG) and 48-channel functional near-infrared spectroscopy (fNIRS) system. We measured brain activation while 16 healthy participants observed three action tasks: (1) grasping a cup with the intention of drinking; (2) grasping a cup with the intention of moving it; and (3) touching a cup with an unclear intention. The EEG and fNIRS source analysis results revealed the dynamic involvement of both the mirror neuron system (MNS) and the theory of mind (ToM)/mentalizing network during action observation. The source analysis results suggested that the extent to which these two systems were engaged was determined by the clarity of the intention of the observed action. Based on the difference in neural activity observed among different action-observation tasks in the first experiment, we conducted a second experiment to classify the neural processes underlying action observation using a feature classification method. We constructed complex brain networks based on the EEG and fNIRS data. Fusing features from both EEG and fNIRS complex brain networks resulted in a classification accuracy of 72.7% for the three action observation tasks. This study provides a theoretical and empirical basis for elucidating the neural mechanisms of action observation and intention understanding, and a feasible method for decoding the underlying neural processes.

摘要

在社会环境中,观察他人的行为对于理解他们在做什么以及他们的意图和感受至关重要。对动作观察的神经基础和解码进行研究,对于理解与动作相关的过程具有重要意义,并且对认知、社会神经科学和人机交互(HMI)都有影响。在当前的研究中,我们首先使用64通道脑电图(EEG)和48通道功能近红外光谱(fNIRS)组合系统,研究了动作观察过程中的时空动态。我们在16名健康参与者观察三项动作任务时测量了大脑激活情况:(1)以喝水为意图抓握杯子;(2)以移动杯子为意图抓握杯子;(3)以不明确意图触摸杯子。EEG和fNIRS源分析结果揭示了动作观察过程中镜像神经元系统(MNS)和心理理论(ToM)/心理化网络的动态参与。源分析结果表明,这两个系统的参与程度取决于所观察动作意图的清晰度。基于在第一个实验中不同动作观察任务之间观察到的神经活动差异,我们进行了第二个实验,使用特征分类方法对动作观察背后的神经过程进行分类。我们基于EEG和fNIRS数据构建了复杂的脑网络。融合来自EEG和fNIRS复杂脑网络的特征,对于三项动作观察任务的分类准确率达到了72.7%。本研究为阐明动作观察和意图理解的神经机制提供了理论和实证基础,以及一种解码潜在神经过程的可行方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17c4/6803538/814f0f8149c8/fnhum-13-00357-g0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验