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Reconfiguration of brain network between resting-state and P300 task

Citation

Li, F and Yi, Y and Liao, Y and Jiang, Y and Si, Y and Song, L and Zhang, T and Yao, D and Zhang, Y and Cao, Z and Xu, P, Reconfiguration of brain network between resting-state and P300 task, IEEE Transactions on Cognitive and Developmental Systems ISSN 2379-8920 (2020) [Refereed Article]


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DOI: doi:10.1109/TCDS.2020.2965135

Abstract

Previous studies explore the power spectra from the resting-state condition to the oddball task, but whether brain network existing significant difference is still unclear. Our study 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 study, electroencephalograms (EEGs) were collected from 24 subjects, who were required to only mentally count the number of target stimulus; afterwards, EEG networks constructed in different bands were compared between baseline and task to evaluate the reconfiguration of functional connectivity. Compared to 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 our study help us to under-stand 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.

Item Details

Item Type:Refereed Article
Keywords:P300, brain reconfiguration, rhythmical activity, brain network
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Neural, Evolutionary and Fuzzy Computation
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
UTAS Author:Cao, Z (Mr Zehong Cao)
ID Code:136631
Year Published:2020
Deposited By:Information and Communication Technology
Deposited On:2020-01-11
Last Modified:2020-01-24
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