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Predicting individual decision-making responses based on single-trial EEG.
- Source :
-
NeuroImage . Feb2020, Vol. 206, pN.PAG-N.PAG. 1p. - Publication Year :
- 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. • A single-trial EEG based approach is developed to predict individual decision-making response. • A supervised learning approach is developed to extract the discriminative spatial network pattern. • The proposed approach achieves the high prediction performances (over 0.88) for two independent EEG datasets. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10538119
- Volume :
- 206
- Database :
- Academic Search Index
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 141240826
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2019.116333