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dc.contributor.authorZhang, D
dc.contributor.authorChen, K
dc.contributor.authorJian, D
dc.contributor.authorYao, L
dc.contributor.authorWang, S
dc.contributor.authorLi, P
dc.date.accessioned2020-03-23T03:14:39Z
dc.date.available2020-03-23T03:14:39Z
dc.date.issued2019
dc.identifier.isbn9781728146034
dc.identifier.issn1550-4786
dc.identifier.doi10.1109/ICDM.2019.00189
dc.identifier.urihttp://hdl.handle.net/10072/392543
dc.description.abstractAs brain dynamics fluctuate considerably across different subjects, it is challenging to design effective handcrafted features based on prior knowledge. Regarding this gap, this paper proposes a Graph-based Convolutional Recurrent Attention Model (G-CRAM) to explore EEG features across different subjects for movement intention recognition. A graph structure is first developed to embed the positioning information of EEG nodes, and then a convolutional recurrent attention model learns EEG features from both spatial and temporal dimensions and adaptively emphasizes on the most distinguishable temporal periods. The proposed approach is validated on two public movement intention EEG datasets. The results show that the GCRAM achieves superior performance to state-of-the-art methods regarding recognition accuracy and ROC-AUC. Furthermore, model interpreting studies reveal the learning process of different neural network components and demonstrate that the proposed model can extract detailed features efficiently.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename2019 IEEE International Conference on Data Mining (ICDM 2019)
dc.relation.ispartofconferencetitleProceedings - IEEE International Conference on Data Mining, ICDM
dc.relation.ispartofdatefrom2019-11-08
dc.relation.ispartofdateto2019-11-11
dc.relation.ispartoflocationBeijing, China
dc.relation.ispartofpagefrom1450
dc.relation.ispartofpageto1455
dc.relation.ispartofvolume2019-November
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleLearning attentional temporal cues of brainwaves with spatial embedding for motion intent detection
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationZhang, D; Chen, K; Jian, D; Yao, L; Wang, S; Li, P, Learning attentional temporal cues of brainwaves with spatial embedding for motion intent detection, Proceedings - IEEE International Conference on Data Mining, ICDM, 2019, 2019-November, pp. 1450-1455
dc.date.updated2020-03-23T03:12:59Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Sen


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