Reconfiguration of Brain Network Between Resting State and P300 Task

• Published in: IEEE transactions on cognitive and developmental systems Vol. 13; no. 2; pp. 383 - 390
• Main Authors: Li, Fali, Yi, Chanlin, Liao, Yuanyuan, Jiang, Yuanling, Si, Yajing, Song, Limeng, Zhang, Tao, Yao, Dezhong, Zhang, Yangsong, Cao, Zehong, Xu, Peng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Brain network; brain reconfiguration; Brain-computer interfaces; Electrodes; Electroencephalography; Hospitals; P300; Power spectra; Reconfiguration; rhythmical activity; Task analysis; Visual tasks
• ISSN: 2379-8920; 2379-8939
• DOI: 10.1109/TCDS.2020.2965135

Previous studies have explored the power spectra from the resting-state condition to the oddball task, but whether significant difference in the brain network exists is still unclear. This article aims to address how the brain reconfigures its architecture from a resting-state condition (i.e., baseline) to the P300 task in the visual oddball task. In this article, electroencephalograms (EEGs) were collected from 24 subjects, who were required to only mentally count the number of target stimulus; afterward, EEG networks constructed in different bands were compared between baseline and task to evaluate the reconfiguration of functional connectivity. Compared with the baseline, our results showed the significantly enhanced delta/theta functional connectivity and decreased alpha default mode network in the progress of brain reconfiguration to the task. Furthermore, the reconfigured coupling strengths were found to relate to P300 amplitudes, which were then regarded as features to train a classifier to differentiate the brain states and the high and low P300 groups with an accuracy of 100% and 77.78%, respectively. The findings of this article help us understand the updates in functional connectivity from resting state to the oddball task, and the reconfigured network structure has the potential for the selection of good subjects for P300-based brain-computer interface.