Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.neuroimage.2011.07.094 | NeuroImage |
Keywords | DocType | Volume |
Workload classification,EEG,Hierarchical Bayes model,Artificial neural network | Journal | 59 |
Issue | ISSN | Citations |
1 | 1053-8119 | 18 |
PageRank | References | Authors |
1.13 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziheng Wang | 1 | 199 | 7.92 |
| Ryan M Hope | 2 | 18 | 1.81 |
| Wang, Zuoguan | 3 | 53 | 3.77 |
| Qiang Ji | 4 | 2780 | 168.90 |
| Wayne D. Gray | 5 | 825 | 133.25 |