Multiple Human EEG Synchronous Analysis in Group Interaction-Prediction Model for Group Involvement and Individual Leadership

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10284)

Abstract

Successful communication relies on the ability to express and obtain information and fast adaptability to the communication that others think has high quality [1]. The one with high exchange quality in group-based communication is generally supposed to have leadership. The leader’s neural mechanism during the communication is not deeply studied in the previous researches. In this paper, a new method is proposed to evaluate the leadership in group activity by utilizing the characteristic of EEG. We collect the brain electrical activity of the group members with non-intrusive high precision wireless EEG acquisition device to reduce the barrier in exchange activity. Through classification of interactive and noninteractive multivariate analysis with multi-person EEG electrode, it’s found that the left temporal lobe cerebral region of leader elected by voting features obvious activation of electrode after receiving messages from others. Further, his α EEG is significantly inhibited and β EEG is obviously activated. This cerebral region is considered to be the one disposing and predicting errors, which indicates that the leader is good at analyzing each person’s information and disposing errors and used the resources for predicting and planning after accepting the problem. Besides, the frontal lobe α wave of the leader during the stage of communication and discussion is inhibited obviously and it is the same as the voting result.

Keywords

EEG Leadership Synchronization and multi-person interaction 

Notes

Acknowledgments

This work is supported by the NSFC Key Program (91520202), and General Program (61375116). This work is also supported by Beijing Advanced Innovation Center for Future Education with grant No. BJAICFE2016IR-003.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.College of Information Science and TechnologyBeijing Normal UniversityBeijingPeople’s Republic of China

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