Cross-Subject Motor Imagery Tasks EEG Signal Classification Employing Multiplex Weighted Visibility Graph and Deep Feature Extraction-Reference-Cited by-同舟云学术

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Cross-Subject Motor Imagery Tasks EEG Signal Classification Employing Multiplex Weighted Visibility Graph and Deep Feature Extraction

Published:2020-01 Issue:1 Volume:4 Page:1-4
ISSN:2475-1472
Container-title:IEEE Sensors Letters
language:
Short-container-title:IEEE Sens. Lett.

Author:

Samanta Kaniska^ORCID,Chatterjee Soumya^ORCID,Bose Rohit^ORCID

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7782634/8948272/08935205.pdf?arnumber=8935205

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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

2. Multi-scale self-attention approach for analysing motor imagery signals in brain-computer interfaces;Journal of Neuroscience Methods;2024-08

3. Time-Frequency Image Representation Aided Deep Feature Extraction-Based Grid Connected Solar PV Fault Classification Framework;Applied Solar Energy;2024-04

4. Weighted Visibility Graph Based WiFi Indoor Positioning Method Using Heuristic Optimization;Turkish Journal of Science and Technology;2024-03-28

5. Online BCI systems: cross-subject motor imagery classification based on weighted time-domain feature extraction methods;Journal of Engineering Design;2024-03-19

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