{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:22:29Z","timestamp":1775470949773,"version":"3.50.1"},"reference-count":0,"publisher":"European Alliance for Innovation n.o.","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872126"],"award-info":[{"award-number":["61872126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017700","name":"Henan Provincial Science and Technology Research Project","doi-asserted-by":"publisher","award":["222102210078"],"award-info":[{"award-number":["222102210078"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["222300420445"],"award-info":[{"award-number":["222300420445"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EAI Endorsed Trans e-Learn"],"abstract":"<jats:p>A\u00a0convolution graph attention model based on self-distillation convolutional graph attention network (SDC-GAT) is proposed\u00a0for multi-channel EEG emotion recognition.\u00a0Firstly, two-dimensional feature matrix based on EEG time-domain features are constructed, and\u00a0the matrix is\u00a0fed into the graph attention neural network to learn the internal connections between electrical brain channels located in different brain regions. Meanwhile, the three-dimensional feature matrix is constructed according to the relative positions of the electrode channels, and the self-distillation network is employed to extract local high-level abstract features containing electrode spatial position information\u00a0from the three-dimensional feature matrix. Finally, outputs of the two networks are integrated\u00a0to determine\u00a0the\u00a0emotional states. Experiments were performed on the DEAP dataset. The experimental results show that the spatial domain information of the electrode channel and the internal connection relationship between different channels are beneficial for emotion recognition. In addition, the proposed model can effectively fuse these information\u00a0to\u00a0improve the performance of multi-channel EEG emotion recognition.<\/jats:p>","DOI":"10.4108\/eetel.4974","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T08:49:22Z","timestamp":1709887762000},"source":"Crossref","is-referenced-by-count":2,"title":["EEG Emotion Recognition Based on Self-Distillation Convolutional Graph Attention Network"],"prefix":"10.4108","volume":"10","author":[{"given":"Hao","family":"Chao","sequence":"first","affiliation":[]},{"given":"Shuqi","family":"Feng","sequence":"additional","affiliation":[]}],"member":"2587","published-online":{"date-parts":[[2024,3,8]]},"container-title":["EAI Endorsed Transactions on e-Learning"],"original-title":[],"link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/publications.eai.eu\/index.php\/el\/article\/download\/4974\/2961","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/publications.eai.eu\/index.php\/el\/article\/download\/4974\/2961","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T17:17:34Z","timestamp":1726939054000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/publications.eai.eu\/index.php\/el\/article\/view\/4974"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,8]]},"references-count":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.4108\/eetel.4974","relation":{},"ISSN":["2032-9253"],"issn-type":[{"value":"2032-9253","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,8]]}}}