| Wang Zhongmin (王忠民)* ** ***,Li Yan*,He Lang* ** ***,He Yan* ** ***,Zhang Rong* ** ***.[J].高技术通讯(英文),2026,32(3):221~230 |
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| 3D position embedding and hierarchical cross-graph fusion for EEG emotion recognition |
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| DOI：10. 3772 / j. issn. 1006-6748. 2026. 03. 001 |
| 中文关键词: |
| 英文关键词: electroencephalography emotion recognition, position embedding, phase-locking value, graph attention network |
| 基金项目: |
| Author Name | Affiliation | | Wang Zhongmin (王忠民)* ** *** | (* School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, P. R. China)
(** Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an 710121, P. R. China)
(*** Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an 710121, P. R. China) | | Li Yan* | | | He Lang* ** *** | | | He Yan* ** *** | | | Zhang Rong* ** *** | |
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| 中文摘要: |
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| 英文摘要: |
| Electroencephalography ( EEG) signals exhibit spatial sparsity and nonlinearity , making the
precise modeling of inter-channel relationships crucial for emotion recognition. Although graph-based
approaches have shown great potential , challenges persist : how to effectively integrate the stable
spatial layout of electrodes with the dynamically changing functional connectivity during emotional
tasks , thereby constructing models that combine discriminative power with neurophysiological inter-
pretability. To address this , we propose a three-dimension (3D) position-embedding graph attention
long-term memory model (PEGLM) , which introduces a hierarchical cross-transformer fusion mod-
ule to adaptively integrate the 3D positional graph of electrodes with a functional connectivity graph
based on phase-locking value (PLV) . This captures the synergistic characteristics of brain anatomi-
cal structure and functional interaction. We further employ a graph attention network (GAT) to ex-
tract spatial-frequency features using differential entropy (DE) and power spectral density ( PSD) ,
and a bidirectional long short-term memory (Bi-LSTM) with attention to model their temporal evolu-
tion. Experiments on DEAP , SEED , and SEED-IV datasets achieve accuracies of 98. 17% ,
97. 37% , and 94. 04% , respectively , outperforming existing graph-based methods. The results
demonstrate that PEGLM not only achieves superior performance , but also provides neurophysiologi-
cal insights into the spatial-functional coupling mechanisms during emotional processing. |
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