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

• Main Authors: Yajing Si, Fali Li, Keyi Duan, Qin Tao, Cunbo Li, Zehong Cao, Yangsong Zhang, Bharat Biswal, Peiyang Li, Dezhong Yao, Peng Xu
• Format: Article
• Language: English
• Published: Elsevier 2020-02-01
• Series: NeuroImage
• Subjects: Decision-making; Electroencephalogram (EEG); Discriminative spatial network pattern; Brain network; Single-trial prediction
• Online Access: http://www.sciencedirect.com/science/article/pii/S1053811919309243

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.