Name
Abstract | ||
|---|---|---|
Human emotion recognition could greatly contribute to human–computer interaction with promising applications in artificial intelligence. One of the challenges in recognition tasks is learning effective representations with stable performances from electroencephalogram (EEG) signals. In this article, we propose a novel deep-learning framework, named channel-fused dense convolutional network, for EE... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCDS.2020.2976112 | IEEE Transactions on Cognitive and Developmental Systems |
Keywords | DocType | Volume |
Electroencephalography,Feature extraction,Emotion recognition,Task analysis,Correlation,Brain modeling,Convolution | Journal | 13 |
Issue | ISSN | Citations |
4 | 2379-8920 | 2 |
PageRank | References | Authors |
0.39 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongke Gao | 1 | 30 | 8.64 |
| Xinmin Wang | 2 | 21 | 1.94 |
| Yuxuan Yang | 3 | 63 | 5.78 |
| Yanli Li | 4 | 9 | 1.64 |
| Kai Ma | 5 | 49 | 18.48 |
| Guanrong Chen | 6 | 12378 | 1130.81 |