EEGNet-MSD: A Sparse Convolutional Neural Network for Efficient EEG-Based Intent Decoding-Reference-Cited by-同舟云学术

EEGNet-MSD: A Sparse Convolutional Neural Network for Efficient EEG-Based Intent Decoding

Published:2023-09-01 Issue:17 Volume:23 Page:19684-19691
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Fu Rongrong¹^ORCID,Wang Zeyi¹^ORCID,Wang Shiwei²,Xu Xuechen¹,Chen Junxiang³^ORCID,Wen Guilin⁴

Affiliation:

1. Department of Electrical Engineering, Measurement Technology and Instrumentation Key Laboratory of Hebei Province, Yanshan University, Qinhuangdao, China

2. School of Photovoltaic Materials, Jiangxi New Energy Technology Institute, Xinyu, China

3. Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA

4. School of Mechanical Engineering, Yanshan University, Qinhuangdao, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province

Central Guidance on Local Science and Technology Development Fund of Hebei Province

Full-Time Introduction of National High-Level Innovation Talents Research Project of Hebei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10235271/10188614.pdf?arnumber=10188614

Reference38 articles.

1. MI-EEGNET: A novel convolutional neural network for motor imagery classification

2. EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

3. LSDD-EEGNet: An efficient end-to-end framework for EEG-based depression detection

4. Optimized Bi-Objective EEG Channel Selection and Cross-Subject Generalization With Brain–Computer Interfaces

5. S-EEGNet: Electroencephalogram Signal Classification Based on a Separable Convolution Neural Network With Bilinear Interpolation

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