@article{wang12neuroImage,
title = {Cross-subject workload classification with a Hierarchical Bayes Model},
author = { Ziheng Wang and Ryan M. Hope and Zuoguan Wang and Qiang Ji and Wayne D. Gray},
year = {2012},
date = {2012-01-01},
journal = {NeuroImage},
volume = {59},
number = {1},
pages = {64-69},
abstract = {Most of the current EEG-based workload classiers are subject-specific; that is, a new classier is built and trained for each human subject. In this paper we introduce a cross-subject workload classier based on a hierarchical Bayes Model. The cross-subject classier 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 classier is stable across the three levels of workload and comparable to a benchmark subject-specific neural network classier.},
keywords = {Articial Neural Network, EEG, Hierarchical Bayes Model, Workload Classication},
pubstate = {published},
tppubtype = {article}
}
Most of the current EEG-based workload classiers are subject-specific; that is, a new classier is built and trained for each human subject. In this paper we introduce a cross-subject workload classier based on a hierarchical Bayes Model. The cross-subject classier 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 classier is stable across the three levels of workload and comparable to a benchmark subject-specific neural network classier.
