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MI3DNet: A Compact CNN for Motor Imagery EEG Classification with Visualizable Dense Layer Parameters

  • Xi'an Jiaotong University

科研成果: 书/报告/会议事项章节会议稿件同行评审

5 引用 (Scopus)

摘要

Electroencephalography (EEG) based Brain Computer Interface (BCI) attracts more and more attention. Motor Imagery (MI) is a popular one among all the EEG paradigms. Building a subject-independent MI EEG classification procedure is a main challenge in practical applications. Recently, Convolutional Neural Network (CNN) has been introduced and achieved state-of-the-art performance in related areas. To extract subject-independent features in MI EEG classification, we propose the MI3DNet, using a remapped signal cubic as the input. Experiments show that MI3DNet has a higher performance with fewer parameters and layers. We also give a method to plot the parameters of the dense layer, and explain its effect.

源语言英语
主期刊名42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
主期刊副标题Enabling Innovative Technologies for Global Healthcare, EMBC 2020
出版商Institute of Electrical and Electronics Engineers Inc.
510-513
页数4
ISBN(电子版)9781728119908
DOI
出版状态已出版 - 7月 2020
活动42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, 加拿大
期限: 20 7月 202024 7月 2020

出版系列

姓名Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
2020-July
ISSN(印刷版)1557-170X

会议

会议42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
国家/地区加拿大
Montreal
时期20/07/2024/07/20

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