Journal
NEUROIMAGE
Volume 59, Issue 1, Pages 64-69Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2011.07.094
Keywords
Workload classification; EEG; Hierarchical Bayes model; Artificial neural network
Funding
- Office of Naval Research [N000141010019]
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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. (C) 2011 Elsevier Inc. All rights reserved.
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