title: Cross-subject workload classification with a hierarchical Bayes model.
abstract: 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 data from a group of subjects. In our work, it was trained and tested on EEG data collected from 8 subjects as they performed the Multi-Attribute Task Battery across three levels of difficulty. The accuracy of this cross-subject classifier is stable across the three levels of workload and comparable to a benchmark subject-specific neural network classifier.
authors.affiliation: Department of Electrical, Computer, & Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
authors.forename: Ziheng
authors.initials: Z
authors.lastname: Wang
authors.forename: Ryan M
authors.initials: RM
authors.lastname: Hope
authors.forename: Zuoguan
authors.initials: Z
authors.lastname: Wang
authors.forename: Qiang
authors.initials: Q
authors.lastname: Ji
authors.forename: Wayne D
authors.initials: WD
authors.lastname: Gray
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D013647:Task Performance and Analysis; D016526:Workload / Q000145:classification*; D055815:Young Adult