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Abstract

Decision-making plays an essential role in the interpersonal interactions and cognitive processing of individuals. There has been increasing interest in being able to predict an individual’s decision-making response (i.e., acceptance or rejection). We proposed an electroencephalogram (EEG)-based computational intelligence framework to predict individual responses. Specifically, the discriminative spatial network pattern (DSNP), a supervised learning approach, was applied to single-trial EEG data to extract the DSNP feature from the single-trial brain network. A linear discriminate analysis (LDA) trained on the DSNP features was then used to predict the individual response trial-by-trial. To verify the performance of the proposed DSNP, we recruited two independent subject groups, and recorded the EEGs using two types of EEG systems. The performances of the trial-by-trial predictors achieved an accuracy of 0.88 ± 0.09 for the first dataset, and 0.90 ± 0.10 for the second dataset. These trial-by-trial prediction performances suggested that individual responses could be predicted trial-by-trial by using the specific pattern of single-trial EEG networks, and our proposed method has the potential to establish the biologically inspired artificial intelligence decision system.

Item Type:	Article
Authors/Creators:	Si, Y and Li, Fali and Duan, K and Tao, Q and Li, C and Cao, Z and Zhang, Yangsong and Biswal, B and Li, P and Yao, D and Xu, P
Keywords:	EEG, decision making, electroencephalogram, discriminative spatial network pattern, brain network, single-trial prediction
Journal or Publication Title:	Neuroimage
Publisher:	Academic Press Inc Elsevier Science
ISSN:	1053-8119
DOI / ID Number:	10.1016/j.neuroimage.2019.116333
Copyright Information:	Copyright 2019 Published by Elsevier Inc.
Related URLs:	Google Scholar: Cao, Z
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