Fusion Graph Representation of EEG for Emotion Recognition
Various relations existing in Electroencephalogram (EEG) data are significant for EEG feature representation. Thus, studies on the graph-based method focus on extracting relevancy between EEG channels. The shortcoming of existing graph studies is that they only consider a single relationship of EEG...
Main Authors: | Menghang Li, Min Qiu, Wanzeng Kong, Li Zhu, Yu Ding |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-01-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/3/1404 |
Similar Items
-
EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph
by: Tianjiao Kong, et al.
Published: (2021-03-01) -
Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition
by: Jing Zhang, et al.
Published: (2022-12-01) -
OGSSL: A Semi-Supervised Classification Model Coupled With Optimal Graph Learning for EEG Emotion Recognition
by: Yong Peng, et al.
Published: (2022-01-01) -
Multi-channel EEG emotion recognition through residual graph attention neural network
by: Hao Chao, et al.
Published: (2023-07-01) -
Semi-supervised regression with adaptive graph learning for EEG-based emotion recognition
by: Tianhui Sha, et al.
Published: (2023-04-01)