Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance During Motor Training-Reference-Cited by-同舟云学术

Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance During Motor Training

Published:2023 Issue: Volume:31 Page:2872-2882
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Wang Zhongpeng¹,Yang Lu²^ORCID,Zhou Yijie²^ORCID,Chen Long²^ORCID,Gu Bin¹,Liu Shuang²,Xu Minpeng¹^ORCID,He Feng¹^ORCID,Ming Dong¹^ORCID

Affiliation:

1. Academy of Medical Engineering and Translational Medicine, the College of Precision Instruments and Optoelectronics Engineering, the Tianjin Center for Brain Science, and the Tianjin International Joint Research Center for Neural Engineering, Tianjin University, Tianjin, China

2. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of Tianjin

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/10031624/10141658.pdf?arnumber=10141658

Reference56 articles.

1. Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury

2. Is Motor-Imagery Brain-Computer Interface Feasible in Stroke Rehabilitation?

3. Optimizing Single-Trial EEG Classification by Stationary Matrix Logistic Regression in Brain–Computer Interface

4. Deep Learning Classification of Neuro-Emotional Phase Domain Complexity Levels Induced by Affective Video Film Clips

5. Modulation of event-related desynchronization during kinematic and kinetic hand movements

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