A Portable and Affordable Four-Channel EEG System for Emotion Recognition with Self-Supervised Feature Learning
Emotions play a pivotal role in shaping human decision-making, behavior, and physiological well-being. Electroencephalography (EEG)-based emotion recognition offers promising avenues for real-time self-monitoring and affective computing applications. However, existing commercial solutions are often...
Saved in:
| Main Authors: | Hao Luo, Haobo Li, Wei Tao, Yi Yang, Chio-In Ieong, Feng Wan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/10/1608 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The Next Frontier in Brain Monitoring: A Comprehensive Look at In-Ear EEG Electrodes and Their Applications
by: Alexandra Stefania Mihai (Ungureanu), et al.
Published: (2025-05-01) -
Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification
by: Ziang Liu, et al.
Published: (2025-06-01) -
CIT-EmotionNet: convolution interactive transformer network for EEG emotion recognition
by: Wei Lu, et al.
Published: (2024-12-01) -
EEG Depression Recognition Based on Multi-domain Features Combined with CBAM Model
by: CHEN Yu, et al.
Published: (2024-06-01) -
Automated Detection of Aberrant Episodes in Epileptic Conditions: Leveraging EEG and Machine Learning Algorithms
by: Uddipan Hazarika, et al.
Published: (2025-03-01)