| Title: |
Singular Learning of Deep Multilayer Perceptrons for EEG-Based Emotion Recognition |
| Authors: |
Weili Guo, Guangyu Li, Jianfeng Lu, Jian Yang |
| Source: |
Frontiers in Computer Science, Vol 3 (2021) |
| Publisher Information: |
Frontiers Media S.A., 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Electronic computers. Computer science |
| Subject Terms: |
emotion recognition, EEG, deep multilayer perceptrons, singular learning, theoretical and numerical analysis, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: |
Human emotion recognition is an important issue in human–computer interactions, and electroencephalograph (EEG) has been widely applied to emotion recognition due to its high reliability. In recent years, methods based on deep learning technology have reached the state-of-the-art performance in EEG-based emotion recognition. However, there exist singularities in the parameter space of deep neural networks, which may dramatically slow down the training process. It is very worthy to investigate the specific influence of singularities when applying deep neural networks to EEG-based emotion recognition. In this paper, we mainly focus on this problem, and analyze the singular learning dynamics of deep multilayer perceptrons theoretically and numerically. The results can help us to design better algorithms to overcome the serious influence of singularities in deep neural networks for EEG-based emotion recognition. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2624-9898 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fcomp.2021.786964/full; https://doaj.org/toc/2624-9898 |
| DOI: |
10.3389/fcomp.2021.786964 |
| Access URL: |
https://doaj.org/article/1958289484c8431f88e62833bf648c3f |
| Accession Number: |
edsdoj.1958289484c8431f88e62833bf648c3f |
| Database: |
Directory of Open Access Journals |
