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Predicting individual decision-making responses based on single-trial EEG

Citation

Si, Y and Li, F and Duan, K and Tao, Q and Li, C and Cao, Z and Zhang, Y and Biswal, B and Li, P and Yao, D and Xu, P, Predicting individual decision-making responses based on single-trial EEG, Neuroimage pp. 1-23. ISSN 1053-8119 (2019) [Refereed Article]


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DOI: doi:10.1016/j.neuroimage.2019.116333

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 Details

Item Type:Refereed Article
Keywords:EEG, decision making, electroencephalogram, discriminative spatial network pattern, brain network, single-trial prediction
Research Division:Information and Computing Sciences
Research Group:Information Systems
Research Field:Computer-Human Interaction
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
UTAS Author:Cao, Z (Mr Zehong Cao)
ID Code:135608
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2019-11-05
Last Modified:2019-11-18
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