{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T18:43:31Z","timestamp":1775846611350,"version":"3.50.1"},"reference-count":65,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments\u2019 accuracy of 99.42% and subject-independent experiments\u2019 accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.<\/jats:p>","DOI":"10.3389\/fncom.2024.1416494","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T04:58:27Z","timestamp":1721365107000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism"],"prefix":"10.3389","volume":"18","author":[{"given":"Wei","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yuan","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Yuanlin","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Liya","family":"Huang","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"6285","DOI":"10.3390\/s20216285","article-title":"Differences in power spectral densities and phase quantities due to processing of EEG signals","volume":"20","author":"Alam","year":"2020","journal-title":"Sensors"},{"key":"ref2","article-title":"Layer normalization","author":"Ba","year":"2016","journal-title":"arXiv"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"729707","DOI":"10.3389\/fnsys.2021.729707","article-title":"Application of electroencephalography-based machine learning in emotion recognition: a review","volume":"15","author":"Cai","year":"2021","journal-title":"Front. 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