Research Progress on the Coding and Decoding of Scalp Electroencephalogram Induced by Movement Intention and Brain-Computer Interface

被引：0
|
作者：
Chen L. [1 ]
Zhang D. [1 ]
Wang K. [1 ]
Xu M. [1 ,2 ]
Ming D. [1 ,2 ]
机构：
[1] Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin
[2] School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2023年 / 45卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Brain-Computer Interface (BCI); Clinical rehabilitation; ElectroEncephaloGram (EEG); Fine motor control; Movement intention;
D O I：
10.11999/JEIT221449
中图分类号：
学科分类号：
摘要：
Movement intention based Brain-Computer Interfaces (BCIs) have important research significance and application value in motor enhancement, replacement and rehabilitation. Among them, Motor Imagery (MI) is the most commonly used BCI paradigm to represent motor intention. However, traditional MI-BCIs usually focus on the recognition of the intention of different limbs, and the classification accuracies are relatively low, which restricts fine motor control and rehabilitation. To solve the above problems, in recent years, researchers have carried out a series of meaningful explorations in coding and decoding of scalp ElectroEncephaloGram (EEG) from three aspects: specific parts of a single limb movement intention, kinematic and kinetics intention, and mismatch between movement and expectation. On the basis of the above research, some typical applications to high freedom motor control and stroke rehabilitation have been developed. The research progress in this field from the related paradigms of scalp EEG coding and decoding of motor intention and its BCI application is reviewed. Besides, the existing challenges and possible solutions are discussed, considering to promote the in-depth research and application of motor intention based BCIs. © 2023 Science Press. All rights reserved.
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页码：3458 / 3467
页数：9
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