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

CHEN Long; ZHANG Dingze; WANG Kun; XU Minpeng; MING Dong

doi:10.11999/JEIT221449

Volume 45 Issue 10

Oct. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(10): 3458-3467

CHEN Long, ZHANG Dingze, WANG Kun, XU Minpeng, MING Dong. Research Progress on the Coding and Decoding of Scalp Electroencephalogram Induced by Movement Intention and Brain-Computer Interface[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3458-3467. doi: 10.11999/JEIT221449

Citation:

CHEN Long, ZHANG Dingze, WANG Kun, XU Minpeng, MING Dong. Research Progress on the Coding and Decoding of Scalp Electroencephalogram Induced by Movement Intention and Brain-Computer Interface[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3458-3467. doi: 10.11999/JEIT221449

Citation:

CHEN Long, ZHANG Dingze, WANG Kun, XU Minpeng, MING Dong. Research Progress on the Coding and Decoding of Scalp Electroencephalogram Induced by Movement Intention and Brain-Computer Interface[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3458-3467. doi: 10.11999/JEIT221449

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

doi: 10.11999/JEIT221449

CHEN Long¹,
ZHANG Dingze¹,
WANG Kun^{1
,
,},
XU Minpeng^{1, 2},
MING Dong^{1, 2}

1.
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
2.
School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 200072, China

Funds: The National Key Research and Development Program of China (2021YFF0602902), The National Natural Science Foundation of China (82001939, 62122059, 81925020, 62206198)

Received Date: 2022-11-17
Rev Recd Date: 2023-04-12

Available Online: 2023-04-24

Publish Date: 2023-10-31

Abstract

Abstract

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.
- Brain-Computer Interface (BCI),
- ElectroEncephaloGram (EEG),
- Movement intention,
- Fine motor control,
- Clinical rehabilitation

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References(58)

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