当前位置: X-MOL 学术Front Hum Neurosci › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Weighted Brain Network Metrics for Decoding Action Intention Understanding Based on EEG
Frontiers in Human Neuroscience ( IF 2.9 ) Pub Date : 2020-07-02 , DOI: 10.3389/fnhum.2020.00232
Xingliang Xiong 1 , Zhenhua Yu 2 , Tian Ma 2 , Ning Luo 3 , Haixian Wang 1 , Xuesong Lu 4 , Hui Fan 5
Affiliation  

Background: Understanding the action intentions of others is important for social and human-robot interactions. Recently, many state-of-the-art approaches have been proposed for decoding action intention understanding. Although these methods have some advantages, it is still necessary to design other tools that can more efficiently classify the action intention understanding signals. New Method: Based on EEG, we first applied phase lag index (PLI) and weighted phase lag index (WPLI) to construct functional connectivity matrices in five frequency bands and 63 micro-time windows, then calculated nine graph metrics from these matrices and subsequently used the network metrics as features to classify different brain signals related to action intention understanding. Results: Compared with the single methods (PLI or WPLI), the combination method (PLI+WPLI) demonstrates some overwhelming victories. Most of the average classification accuracies exceed 70%, and some of them approach 80%. In statistical tests of brain network, many significantly different edges appear in the frontal, occipital, parietal, and temporal regions. Conclusions: Weighted brain networks can effectively retain data information. The integrated method proposed in this study is extremely effective for investigating action intention understanding. Both the mirror neuron and mentalizing systems participate as collaborators in the process of action intention understanding.

中文翻译:

基于脑电图解码动作意图理解的加权脑网络度量

背景:了解他人的行动意图对于社交和人机交互很重要。最近,已经提出了许多最先进的方法来解码动作意图理解。虽然这些方法都有一些优势,但仍然需要设计其他工具来更有效地对动作意图理解信号进行分类。新方法:基于脑电图,我们首先应用相位滞后指数(PLI)和加权相位滞后指数(WPLI)构建五个频段和63个微时间窗口的功能连接矩阵,然后从这些矩阵中计算出九个图形度量,随后使用网络度量作为特征对与动作意图理解相关的不同大脑信号进行分类。结果:与单一方法(PLI 或 WPLI)相比,组合方法(PLI+WPLI)展示了一些压倒性的胜利。大多数平均分类准确率超过70%,有的接近80%。在大脑网络的统计测试中,额叶、枕叶、顶叶和颞叶区域出现了许多显着不同的边缘。结论:加权脑网络可以有效地保留数据信息。本研究中提出的综合方法对于调查行动意图理解非常有效。镜像神经元和心理化系统都作为合作者参与了行动意图理解的过程。枕叶、顶叶和颞区。结论:加权脑网络可以有效地保留数据信息。本研究中提出的综合方法对于调查行动意图理解非常有效。镜像神经元和心理化系统都作为合作者参与了行动意图理解的过程。枕叶、顶叶和颞区。结论:加权脑网络可以有效地保留数据信息。本研究中提出的综合方法对于调查行动意图理解非常有效。镜像神经元和心理化系统都作为合作者参与了行动意图理解的过程。
更新日期:2020-07-02
down
wechat
bug