Name
Abstract | ||
|---|---|---|
Task engagement is delined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overlitting. In this paper, we proposed two deep models (i.e., a deep clas... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/THMS.2016.2608933 | IEEE Transactions on Human-Machine Systems |
Keywords | DocType | Volume |
Electroencephalography,Brain models,Data models,Training,Machine learning,Feature extraction | Journal | 47 |
Issue | ISSN | Citations |
4 | 2168-2291 | 6 |
PageRank | References | Authors |
0.51 | 14 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Li | 1 | 338 | 49.66 |
| Guangfan Zhang | 2 | 39 | 4.64 |
| Wei Wang | 3 | 29 | 1.67 |
| Roger Xu | 4 | 111 | 14.71 |
| Tom Schnell | 5 | 29 | 1.67 |
| Jonathan Wen | 6 | 6 | 0.51 |
| Frederic D Mckenzie | 7 | 75 | 18.51 |
| Jiang Li | 8 | 251 | 27.28 |