Learning attentional temporal cues of brainwaves with spatial embedding for motion intent detection

Dalin Zhang, Kaixuan Chen, Debao Jian, Lina Yao, Sen Wang, Po Li

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10 Citationer (Scopus)

Abstract

As 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.

OriginalsprogEngelsk
TitelProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
RedaktørerJianyong Wang, Kyuseok Shim, Xindong Wu
Antal sider6
ForlagIEEE (Institute of Electrical and Electronics Engineers)
Publikationsdatonov. 2019
Sider1450-1455
Artikelnummer8970671
ISBN (Elektronisk)9781728146034
DOI
StatusUdgivet - nov. 2019
Udgivet eksterntJa
Begivenhed19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, Kina
Varighed: 8 nov. 201911 nov. 2019

Konference

Konference19th IEEE International Conference on Data Mining, ICDM 2019
Land/OmrådeKina
ByBeijing
Periode08/11/201911/11/2019
SponsorBaidu, et al., IEEE Computer Society, LinkedIn, MiningLamp Technology, US National Science Foundation (NSF)
NavnProceedings - IEEE International Conference on Data Mining, ICDM
Vol/bind2019-November
ISSN1550-4786

Bibliografisk note

Publisher Copyright:
© 2019 IEEE.

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