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

Author:

Lee Minji1ORCID,Kim Yun-Hee2ORCID,Lee Seong-Whan3ORCID

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

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1. Riemannian Locality Preserving Method for Transfer Learning With Applications on Brain-Computer Interface;IEEE Journal of Biomedical and Health Informatics;2024-08

2. A Survey of Wearable Lower Extremity Neurorehabilitation Exoskeleton: Sensing, Gait Dynamics, and Human–Robot Collaboration;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2024-06

3. Towards Motor Imagery Classification of Finger Tapping by Both Hands for Controlling a Finger-Arm Bionic Robot;2024 12th International Winter Conference on Brain-Computer Interface (BCI);2024-02-26

4. Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024

5. Network Properties Analysis in Stroke Patients Based on the resting-state EEG;Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering;2023-11-09

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