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

Document Type

Article

Publication Date

2-1-2020

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.

Identifier

85075477246 (Scopus)

Publication Title

Neuroimage

External Full Text Location

https://doi.org/10.1016/j.neuroimage.2019.116333

e-ISSN

10959572

ISSN

10538119

PubMed ID

31698078

Volume

206

Grant

HNBBL17001

Fund Ref

School for Advanced Research

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