Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition
Emotion recognition based on multichannel electroencephalogram (EEG) signals is a key research area in the field of affective computing. Traditional methods extract EEG features from each channel based on extensive domain knowledge and ignore the spatial characteristics and global synchronization in...
Saved in:
| Main Authors: | Hao Chao, Liang Dong, Yongli Liu, Baoyun Lu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6816502 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Portable and Affordable Four-Channel EEG System for Emotion Recognition with Self-Supervised Feature Learning
by: Hao Luo, et al.
Published: (2025-05-01) -
Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data
by: asmaa Bashar Hmaza, et al.
Published: (2024-03-01) -
Domain adaptive deep possibilistic clustering for EEG-based emotion recognition
by: Yufang Dan, et al.
Published: (2025-07-01) -
An Effective Deep Neural Network Architecture for EEG-Based Recognition of Emotions
by: Khadidja Henni, et al.
Published: (2025-01-01) -
MSBiLSTM-Attention: EEG Emotion Recognition Model Based on Spatiotemporal Feature Fusion
by: Yahong Ma, et al.
Published: (2025-03-01)