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<p>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 <a href="https://github.com/czxyhll/DGLFS">https://github.com/czxyhll/DGLFS</a>.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月13日

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  • Liangliang Hu,
  • Congming Tan,
  • Yin Tian

摘要

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.