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Cross-subject workload classification with a hierarchical Bayes model
- Source :
-
NeuroImage . Jan2012, Vol. 59 Issue 1, p64-69. 6p. - Publication Year :
- 2012
-
Abstract
- 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 10538119
- Volume :
- 59
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 66672064
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2011.07.094