Cross-subject workload classification with a hierarchical Bayes model

• Published in: NeuroImage (Orlando, Fla.) Vol. 59; no. 1; pp. 64 - 69
• Main Authors: Wang, Ziheng, Hope, Ryan M., Wang, Zuoguan, Ji, Qiang, Gray, Wayne D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 02.01.2012; Elsevier Limited
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural network; Bayes Theorem; Bayesian analysis; EEG; Electroencephalography; Female; Hierarchical Bayes model; Humans; Male; Methods; Models, Theoretical; Neural networks; Normal distribution; Pattern Recognition, Automated - methods; Studies; Task Performance and Analysis; Workload - classification; Young Adult; Artificial neural network; Hierarchical Bayes model; Workload classification; EEG

Most of the current EEG-based workload classifiers are subject-specific; that is, a new classifier is built and trained for each human subject. In this paper we introduce a cross-subject workload classifier based on a hierarchical Bayes model. The cross-subject classifier is trained and tested with...