Motor Impairment in Stroke Patients Is Associated With Network Properties During Consecutive Motor Imagery-Reference-Cited by-同舟云学术

Motor Impairment in Stroke Patients Is Associated With Network Properties During Consecutive Motor Imagery

Published:2022-08 Issue:8 Volume:69 Page:2604-2615
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Lee Minji¹^ORCID,Kim Yun-Hee²^ORCID,Lee Seong-Whan³^ORCID

Affiliation:

1. Department of Brain and Cognitive Engineering, Korea University, Republic of Korea

2. Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, the Department of Health Science and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea

3. Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea

Funder

Institute for Information & Communications Technology Promotion

Development of BCI based Brain and Cognitive Computing Technology for Recognizing User's Intentions

Department of Artificial Intelligence, Korea University

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/9832680/09714797.pdf?arnumber=9714797

Reference64 articles.

1. Comparison of best and worst performance on EEG coherence in skilled dart players;hung;Psychoph,2009

2. STRUCTURAL ORGANIZATION OF FUNCTIONAL NETWORKS FROM EEG SIGNALS DURING MOTOR LEARNING TASKS

3. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

4. The assessment and analysis of handedness: The Edinburgh inventory

5. Lateralization of motor imagery following stroke

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3. Improved Domain Adaptation Network Based on Wasserstein Distance for Motor Imagery EEG Classification;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2023

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