title: An EEG workload classifier for multiple subjects.
abstract: EEG data has been used to discriminate levels of mental workload when classifiers are created for each subject, but the reliability of classifiers trained on multiple subjects has yet to be investigated. Artificial neural network and naive Bayesian classifiers were trained with data from single and multiple subjects and their ability to discriminate among three difficulty conditions was tested. When trained on data from multiple subjects, both types of classifiers poorly discriminated between the three levels. However, a novel model, the naive Bayesian classifier with a hidden node, performed nearly as well as the models trained and tested on individuals. In addition, a hierarchical Bayes model with a higher level constraint on the hidden node can further improve its performance.
authors.affiliation: Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute.
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: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D001499:Bayes Theorem; D003199:Computer Systems; D004569:Electroencephalography / Q000379:methods*; D004867:Equipment Design; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D016011:Normal Distribution; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted