FBMSNet: A Filter-Bank Multi-Scale Convolutional Neural Network for EEG-Based Motor Imagery Decoding-Reference-Cited by-同舟云学术

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FBMSNet: A Filter-Bank Multi-Scale Convolutional Neural Network for EEG-Based Motor Imagery Decoding

Published:2023-02 Issue:2 Volume:70 Page:436-445
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Liu Ke¹^ORCID,Yang Mingzhao¹^ORCID,Yu Zhuliang²^ORCID,Wang Guoyin¹^ORCID,Wu Wei³^ORCID

Affiliation:

1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, China

2. College of Automation Science and Engineering, South China University of Technology, China

3. College of Automation Science and Engineering, South China University of Technology, Guangzhou, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Chongqing

Technology Innovation 2030

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/10021907/09837422.pdf?arnumber=9837422

Reference36 articles.

1. Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

2. InceptionTime: Finding AlexNet for time series classification

3. Rethinking the Inception Architecture for Computer Vision

4. Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients

5. A Novel MI-EEG Imaging With the Location Information of Electrodes

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1. Enhancing motor imagery decoding in brain–computer interfaces using Riemann tangent space mapping and cross frequency coupling;Biomedical Signal Processing and Control;2025-01

2. Selective regularized spatial features representation learning for motor imagery EEG based on alternating cascaded model;Applied Soft Computing;2024-11

3. EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification;Neuroscience;2024-09

4. Deep learning in motor imagery EEG signal decoding: A Systematic Review;Neurocomputing;2024-09

5. MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification;IEEE Internet of Things Journal;2024-09-01

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