Decoding Human Interaction Type From Inter-Brain Synchronization by Using EEG Brain Network

被引:0
|
作者
Wang, Xiangcun [1 ]
Shi, Ran [1 ]
Wu, Xia [1 ]
Zhang, Jiacai [1 ]
机构
[1] Beijing Normal Univ, Sch Artificial Intelligence, Beijing 100875, Peoples R China
关键词
EEG hyper-scanning; interpersonal interaction; inter-brain synchronization; neural decoding; COOPERATION;
D O I
10.1109/JBHI.2023.3329742
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cooperation and competition are two common forms of interpersonal interactions and exploring inter-brain synchronization in these two forms can help to further deliberate the underlying neural mechanisms of interpersonal interactions. Recently, studies revealed that electrode-paired inter-brain synchronization plays an important role in human interactions. This study investigated the neural correlates of interpersonal synchronization at the brain network scale and interaction type. Firstly, the network-wise inter-brain synchronization (NIBS) index reflecting cross-brain network synchronization from the global brain perspective was advanced. Secondly, statistical analysis demonstrated that there are differences in NIBS activities between cooperative and competitive interactions. And a row-filtered depthwise separable convolution network was proposed to classify the NIBS features. Results of EEG hyper-scanning data showed significant differences in NIBS between cooperative and competitive tasks, and a comparative study manifested that the cross-brain synchronization in cooperative tasks is more consistent than that of competitive tasks. The neural decoder using a modified convolution network achieved a peak accuracy of 96.05% under the binary classification(cooperation vs competition).
引用
收藏
页码:204 / 215
页数:12
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