Name
Abstract | ||
|---|---|---|
Convolutional Neural Networks (CNNs) have achieved remarkable performance breakthroughs in a variety of tasks. Recently, CNN-based methods that are fed with hand-extracted EEG features have steadily improved their performance on the emotion recognition task. In this paper, we propose a novel convolutional layer, called the Scaling Layer, which can adaptively extract effective data-driven spectrogram-like features from raw EEG signals. Furthermore, it exploits convolutional kernels scaled from one data-driven pattern to exposed a frequency-like dimension to address the shortcomings of prior methods requiring hand-extracted features or their approximations. ScalingNet, the proposed neural network architecture based on the Scaling Layer, has achieved state-of-the-art results across the established DEAP and AMIGOS benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.08.018 | Neurocomputing |
Keywords | DocType | Volume |
Deep learning,Convolutional Neural Networks,EEG,Emotion recognition,ScalingNet | Journal | 463 |
ISSN | Citations | PageRank |
0925-2312 | 1 | 0.37 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingzhao Hu | 1 | 1 | 0.71 |
| Chen Wang | 2 | 361 | 93.70 |
| Qiaomei Jia | 3 | 1 | 0.71 |
| Qirong Bu | 4 | 1 | 1.38 |
| Jun Feng | 5 | 44 | 16.44 |