運(yùn)動意圖的頭皮腦電編解碼及其腦-機(jī)接口研究進(jìn)展
doi: 10.11999/JEIT221449
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天津大學(xué)醫(yī)學(xué)工程與轉(zhuǎn)化醫(yī)學(xué)研究院 天津 300072
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天津大學(xué)精密儀器與光電子工程學(xué)院 天津 300072
Research Progress on the Coding and Decoding of Scalp Electroencephalogram Induced by Movement Intention and Brain-Computer Interface
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Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
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School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 200072, China
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摘要: 基于運(yùn)動意圖的腦-機(jī)接口(BCI)對人體運(yùn)動功能增強(qiáng)、替代和康復(fù)具有重要研究意義與應(yīng)用價(jià)值。其中,運(yùn)動想象(MI)是最常用的表征運(yùn)動意圖的BCI范式。然而,傳統(tǒng)MI-BCI通常僅實(shí)現(xiàn)不同肢體部位運(yùn)動意圖解碼,且識別正確率較低,制約著精細(xì)運(yùn)動控制與康復(fù)效果。針對上述問題,近年來研究者在單一肢體特定部位、運(yùn)動學(xué)與動力學(xué)意圖誘發(fā)頭皮腦電編解碼以及運(yùn)動意圖錯誤相關(guān)電位檢測3個方面開展了一系列有意義的探索,并在高自由度的運(yùn)動指令控制和面向卒中患者的臨床康復(fù)應(yīng)用方面取得了較大的研究成果。該文從運(yùn)動意圖的頭皮腦電(EEG)編解碼相關(guān)范式及其BCI應(yīng)用兩個方面綜述了本領(lǐng)域研究進(jìn)展,并探討當(dāng)前研究存在的問題和可能的解決方案,以期促進(jìn)運(yùn)動意圖BCI技術(shù)的深入研究及開發(fā)應(yīng)用。
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關(guān)鍵詞:
- 腦-機(jī)接口 /
- 腦電 /
- 運(yùn)動意圖 /
- 精細(xì)運(yùn)動控制 /
- 臨床康復(fù)
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. -
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