Adaptive dual-graph learning joint feature selection for EEG emotion recognition

Emotion recognition based on electroencephalography (EEG) has garnered increasing attention for its ability to reflect human emotional states objectively and in real time. However, EEG signals exhibit significant variations across different subjects and experimental sessions, posing challenges to th...

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Published in:Journal of King Saud University. Computer and information sciences Vol. 37; no. 4; pp. 69 - 19
Main Authors: Hu, Liangliang, Tan, Congming, Tian, Yin
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.06.2025
Springer Nature B.V
Springer
Subjects:
Adaptation
Algorithms
Biomarkers
Brain research
Cognition
Computer Imaging
Computer Science
Database Management
EEG
Electroencephalography
Emotion recognition
Emotional factors
Emotions
Feature selection
Global structure graph
Invariants
Labels
Local similarity graph
Machine Learning
Optimization
Original Paper
Pattern Recognition and Graphics
Physiology
Propagation
Regularization
Software Engineering/Programming and Operating Systems
Systems and Data Security
Theory of Computation
Vision
Emotion recognition
Feature selection
Global structure graph
EEG
Local similarity graph
ISSN:1319-1578, 2213-1248, 1319-1578
Online Access:Get full text
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Summary:Emotion recognition based on electroencephalography (EEG) has garnered increasing attention for its ability to reflect human emotional states objectively and in real time. However, EEG signals exhibit significant variations across different subjects and experimental sessions, posing challenges to the generalization of emotion recognition algorithms to unseen scenarios. To relieve this issue, we designed a novel hybrid EEG emotion recognition model named DGLFS, which integrates domain-invariant feature selection, label propagation, and adaptive dual-graph regularization into a unified optimization framework. Domain-invariant feature selection projects EEG data from different domains into a shared subspace, capturing emotion-related features that are domain-independent, thereby effectively mitigating data differences across subjects and sessions. Adaptive dual-graph learning simultaneously constructs a local similarity graph and a global structural graph to comprehensively capture both local similarities and global dependencies among EEG samples. Additionally, a graph-based semi-supervised label propagation method is employed, leveraging both global and local structural information embedded in the dual graphs to propagate emotional labels from a small subset of labeled data to unlabeled samples, thereby enabling more accurate emotion estimation in the target domain. We conducted extensive experiments on SEED-IV and SEED-V datasets involving cross-subject and cross-session tasks. The experimental results validate that DGLFS are superior to competitive algorithms in classification accuracy. Moreover, the intrinsic activation patterns revealed by DGLFS are consistent with emotional cognition. The code of DGLFS will be available at https://github.com/czxyhll/DGLFS .
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ISSN:1319-1578
2213-1248
1319-1578
DOI:10.1007/s44443-025-00076-5