运动意图的头皮脑电编解码及其脑-机接口研究进展

陈龙; 张定泽; 王坤; 许敏鹏; 明东

doi:10.11999/JEIT221449

运动意图的头皮脑电编解码及其脑-机接口研究进展

doi: 10.11999/JEIT221449

陈龙¹,
张定泽¹,
王坤^1, ,,
许敏鹏^{1, 2},
明东^{1, 2}

1.
天津大学医学工程与转化医学研究院天津 300072
2.
天津大学精密仪器与光电子工程学院天津 300072

基金项目: 国家重点研发计划(2021YFF0602902)，国家自然科学基金(82001939, 62122059, 81925020, 62206198)

详细信息

作者简介:
陈龙：男，副教授，研究方向为脑机接口、神经调控与康复

张定泽：男，硕士生，研究方向为运动意图脑电编解码

王坤：女，讲师，研究方向为运动意图脑电信号特征提取及其脑-机接口设计

许敏鹏：男，教授，研究方向为脑-机接口及其应用转化

明东：男，教授，研究方向为神经工程

通讯作者:
王坤　flora_wk@tju.edu.cn

中图分类号: TN911.7; TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-17
- 修回日期: 2023-04-12
- 网络出版日期: 2023-04-24
- 刊出日期: 2023-10-31

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

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)

摘要

摘要: 基于运动意图的脑-机接口(BCI)对人体运动功能增强、替代和康复具有重要研究意义与应用价值。其中，运动想象(MI)是最常用的表征运动意图的BCI范式。然而，传统MI-BCI通常仅实现不同肢体部位运动意图解码，且识别正确率较低，制约着精细运动控制与康复效果。针对上述问题，近年来研究者在单一肢体特定部位、运动学与动力学意图诱发头皮脑电编解码以及运动意图错误相关电位检测3个方面开展了一系列有意义的探索，并在高自由度的运动指令控制和面向卒中患者的临床康复应用方面取得了较大的研究成果。该文从运动意图的头皮脑电(EEG)编解码相关范式及其BCI应用两个方面综述了本领域研究进展，并探讨当前研究存在的问题和可能的解决方案，以期促进运动意图BCI技术的深入研究及开发应用。
- 脑-机接口 /
- 脑电 /
- 运动意图 /
- 精细运动控制 /
- 临床康复
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

HTML全文

图 1 单一肢体特定部位的运动意图编解码

下载: 全尺寸图片幻灯片

图 2 运动学与动力学意图编解码

下载: 全尺寸图片幻灯片

图 3 运动意图错误相关电位检测

下载: 全尺寸图片幻灯片

图 4 高自由度运动指令控制

下载: 全尺寸图片幻灯片

参考文献(58)

[1]	何庆华, 彭承琳, 吴宝明. 脑机接口技术研究方法[J]. 重庆大学学报:自然科学版, 2002, 25(12): 106–109. doi: 10.3969/j.issn.1000-582X.2002.12.030 HE Qinghua, PENG Chenglin, and WU Baoming. Research methods of brain-computer interface technology[J]. Journal of Chongqing University:Natural Science Edition, 2002, 25(12): 106–109. doi: 10.3969/j.issn.1000-582X.2002.12.030
[2]	XU Minpeng, HAN Jin, WANG Yijun, et al. Implementing over 100 command codes for a high-speed hybrid brain-computer interface using concurrent P300 and SSVEP features[J]. IEEE Transactions on Biomedical Engineering, 2020, 67(11): 3073–3082. doi: 10.1109/TBME.2020.2975614
[3]	XU Minpeng, XIAO Xiaolin, WANG Yijun, et al. A brain-computer interface based on miniature-event-related potentials induced by very small lateral visual stimuli[J]. IEEE Transactions on Biomedical Engineering, 2018, 65(5): 1166–1175. doi: 10.1109/TBME.2018.2799661
[4]	MENG Jiayuan, XU Minpeng, WANG Kun, et al. Separable EEG features induced by timing prediction for active brain-computer interfaces[J]. Sensors, 2020, 20(12): 3588. doi: 10.3390/s20123588
[5]	张力新, 张珊珊, 王坤, 等. 运动相关思维诱发脑电信息解码与应用综述[J]. 仪器仪表学报, 2019, 40(1): 1–11. doi: 10.19650/j.cnki.cjsi.J1804309 ZHANG Lixin, ZHANG Shanshan, WANG Kun, et al. Review on the decoding and application of electroencephalography information induced by motor-related mental activity[J]. Chinese Journal of Scientific Instrument, 2019, 40(1): 1–11. doi: 10.19650/j.cnki.cjsi.J1804309
[6]	NG A K, ANG K K, TEE K P, et al. Optimizing low-frequency common spatial pattern features for multi-class classification of hand movement directions[C]. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 2013: 2780–2783.
[7]	ROBINSON N, GUAN Cuntai, VINOD A P, et al. Multi-class EEG classification of voluntary hand movement directions[J]. Journal of Neural Engineering, 2013, 10(5): 056018. doi: 10.1088/1741-2560/10/5/056018
[8]	YUAN Han, PERDONI C, and HE Bin. Relationship between speed and EEG activity during imagined and executed hand movements[J]. Journal of Neural Engineering, 2010, 7(2): 026001. doi: 10.1088/1741-2560/7/2/026001
[9]	JOCHUMSEN M, NIAZI I K, MRACHACZ-KERSTING N, et al. Detection and classification of movement-related cortical potentials associated with task force and speed[J]. Journal of Neural Engineering, 2013, 10(5): 056015. doi: 10.1088/1741-2560/10/5/056015
[10]	YIN Xuxian, XU Baolei, JIANG Changhao, et al. A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching[J]. Journal of Neural Engineering, 2015, 12(3): 036004. doi: 10.1088/1741-2560/12/3/036004
[11]	YIN Xuxian, XU Baolei, JIANG Changhao, et al. NIRS-based classification of clench force and speed motor imagery with the use of empirical mode decomposition for BCI[J]. Medical Engineering & Physics, 2015, 37(3): 280–286. doi: 10.1016/j.medengphy.2015.01.005
[12]	李玉, 熊馨, 李昭阳, 等. 基于功能性近红外光谱识别右脚三种想象动作研究[J]. 生物医学工程学杂志, 2020, 37(2): 262–270. doi: 10.7507/1001-5515.201905001 LI Yu, XIONG Xin, LI Zhaoyang, et al. Recognition of three different imagined movement of the right foot based on functional near-infrared spectroscopy[J]. Journal of Biomedical Engineering, 2020, 37(2): 262–270. doi: 10.7507/1001-5515.201905001
[13]	WANG Kun, XU Minpeng, WANG Yijun, et al. Enhance decoding of pre-movement EEG patterns for brain-computer interfaces[J]. Journal of Neural Engineering, 2020, 17(1): 016033. doi: 10.1088/1741-2552/ab598f
[14]	EDELMAN B J, BAXTER B, and HE Bin. EEG source imaging enhances the decoding of complex right-hand motor imagery tasks[J]. IEEE Transactions on Biomedical Engineering, 2016, 63(1): 4–14. doi: 10.1109/TBME.2015.2467312
[15]	MILLER K J, ZANOS S, FETZ E E, et al. Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans[J]. Journal of Neuroscience, 2009, 29(10): 3132–3137. doi: 10.1523/JNEUROSCI.5506-08.2009
[16]	XIAO Ran and DING Lei. Evaluation of EEG features in decoding individual finger movements from one hand[J]. Computational and Mathematical Methods in Medicine, 2013, 2013: 243257. doi: 10.1155/2013/243257
[17]	SALEHI S S M, MOGHADAMFALAHI M, QUIVIRA F, et al. Decoding complex imagery hand gestures[C]. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea, 2017: 2968–2971.
[18]	MWATA-VELU T, AVINA-CERVANTES J G, CRUZ-DUARTE J M, et al. Imaginary finger movements decoding using empirical mode decomposition and a stacked BiLSTM architecture[J]. Mathematics, 2021, 9(24): 3297. doi: 10.3390/MATH9243297
[19]	LIU Kunjia, YU Yang, LIU Yadong, et al. EEG-based motor imagery differing in task complexity[C]. 7th International Conference on Intelligence Science and Big Data Engineering, Dalian, China, 2017: 608–618.
[20]	CHEN Zhitang, WANG Zhongpeng, WANG Kun, et al. Recognizing motor imagery between hand and forearm in the same limb in a hybrid brain computer interface paradigm: An online study[J]. IEEE Access, 2019, 7: 59631–59639. doi: 10.1109/ACCESS.2019.2915614
[21]	MOHAMED A K, MARWALA T, and JOHN L R. Single-trial EEG discrimination between wrist and finger movement imagery and execution in a sensorimotor BCI[C]. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, USA, 2011: 6289–6293.
[22]	KANDEL E R, SCHWARTZ J H, and JESSELL T M. Principles of Neural Science[M]. 4th ed. New York, USA: McGraw-Hill, 2000.
[23]	WANG Jiarong, BI Luzheng, and FEI Weijie. Using non-linear dynamics of EEG signals to classify primary hand movement intent under opposite hand movement[J]. Frontiers in Neurorobotics, 2022, 16: 845127. doi: 10.3389/fnbot.2022.845127
[24]	BENZY V K, VINOD A P, SUBASREE R, et al. Motor imagery hand movement direction decoding using brain computer interface to aid stroke recovery and rehabilitation[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(12): 3051–3062. doi: 10.1109/TNSRE.2020.3039331
[25]	CHOUHAN T, ROBINSON N, VINOD A P, et al. Wavlet phase-locking based binary classification of hand movement directions from EEG[J]. Journal of Neural Engineering, 2018, 15(6): 066008. doi: 10.1088/1741-2552/aadeed
[26]	SOSNIK R and BEN ZUR O. Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG slow cortical potentials[J]. Journal of Neural Engineering, 2020, 17(1): 016065. doi: 10.1088/1741-2552/ab59a7
[27]	MONDINI V, KOBLER R J, SBURLEA A I, et al. Continuous low-frequency EEG decoding of arm movement for closed-loop, natural control of a robotic arm[J]. Journal of Neural Engineering, 2020, 17(4): 046031. doi: 10.1088/1741-2552/aba6f7
[28]	ROBINSON N, CHESTER W J, and SMITHA K G. Use of mobile EEG in decoding hand movement speed and position[J]. IEEE Transactions on Human-Machine Systems, 2021, 51(2): 120–129. doi: 10.1109/THMS.2021.3056274
[29]	CRAMER S C, WEISSKOFF R M, SCHAECHTER J D, et al. Motor cortex activation is related to force of squeezing[J]. Human Brain Mapping, 2002, 16(4): 197–205. doi: 10.1002/hbm.10040
[30]	WANG Kun, WANG Zhongpeng, GUO Yi, et al. A brain-computer interface driven by imagining different force loads on a single hand: An online feasibility study[J]. Journal of Neuroengineering and Rehabilitation, 2017, 14(1): 93. doi: 10.1186/s12984-017-0307-1
[31]	FU Yunfa, CHEN Jian, and XIONG Xin. Calculation and analysis of microstate related to variation in executed and imagined movement of force of hand clenching[J]. Computational Intelligence and Neuroscience, 2018, 2018: 9270685. doi: 10.1155/2018/9270685
[32]	XIONG Xin, FU Yunfa, CHEN Jian, et al. Single-trial recognition of imagined forces and speeds of hand clenching based on brain topography and brain network[J]. Brain Topography, 2019, 32(2): 240–254. doi: 10.1007/s10548-018-00696-3
[33]	USAMA N, KUNZ LEERSKOV K, NIAZI I K, et al. Classification of error-related potentials from single-trial EEG in association with executed and imagined movements: A feature and classifier investigation[J]. Medical & Biological Engineering & Computing, 2020, 58(11): 2699–2710. doi: 10.1007/s11517-020-02253-2
[34]	FARABBI A, ALOIA V, and MAINARDI L. ARX-based EEG data balancing for error potential BCI[J]. Journal of Neural Engineering, 2022, 19(3): 036023. doi: 10.1088/1741-2552/ac6d7f
[35]	KUMAR A, GAO Lin, PIROGOVA E, et al. A review of error-related potential-based brain-computer interfaces for motor impaired people[J]. IEEE Access, 2019, 7: 142451–142466. doi: 10.1109/ACCESS.2019.2944067
[36]	LOPES-DIAS C, SBURLEA A I, and MÜLLER-PUTZ G R. Online asynchronous decoding of error-related potentials during the continuous control of a robot[J]. Scientific Reports, 2019, 9(1): 17596. doi: 10.1038/s41598-019-54109-x
[37]	ZHANG Huaijian, CHAVARRIAGA R, GHEORGHE L, et al. Inferring driver's turning direction through detection of error related brain activity[C]. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 2013: 2196–2199.
[38]	ITURRATE I, GRIZOU J, OMEDES J, et al. Exploiting task constraints for self-calibrated brain-machine interface control using error-related potentials[J]. PLoS One, 2015, 10(7): e0131491. doi: 10.1371/journal.pone.0131491
[39]	EHRLICH S K and CHENG G. Human-agent co-adaptation using error-related potentials[J]. Journal of Neural Engineering, 2018, 15(6): 066014. doi: 10.1088/1741-2552/aae069
[40]	KREILINGER A, NEUPER C, PFURTSCHELLER G, et al. Implementation of error detection into the graz-brain-computer interface, the interaction error potential[C]. Assistive Technology from Adapted Equipment to Inclusive Environments, Florenz, Italy, 2009: 195–199.
[41]	PARASHIVA P K and VINOD A P. Improving direction decoding accuracy during online motor imagery based brain-computer interface using error-related potentials[J]. Biomedical Signal Processing and Control, 2022, 74: 103515. doi: 10.1016/J.BSPC.2022.103515
[42]	BHATTACHARYYA S, KONAR A, and TIBAREWALA D N. Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose[J]. Medical & Biological Engineering & Computing, 2014, 52(12): 1007–1017. doi: 10.1007/s11517-014-1204-4
[43]	NOURMOHAMMADI A, JAFARI M, and ZANDER T O. A survey on unmanned aerial vehicle remote control using brain-computer interface[J]. IEEE Transactions on Human-Machine Systems, 2018, 48(4): 337–348. doi: 10.1109/THMS.2018.2830647
[44]	OSBORN L E, DING Keqin, HAYS M A, et al. Sensory stimulation enhances phantom limb perception and movement decoding[J]. Journal of Neural Engineering, 2020, 17(5): 056006. doi: 10.1088/1741-2552/abb861
[45]	MENG Jianjun, ZHANG Shuying, BEKYO A, et al. Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks[J]. Scientific Reports, 2016, 6: 38565. doi: 10.1038/srep38565
[46]	韩锦, 董博文, 刘邈, 等. 基于P300-SSVEP的双人协同脑-控机械臂汉字书写系统[J]. 数据采集与处理, 2022, 37(6): 1401–1411. doi: 10.16337/j.1004-9037.2022.06.020 HAN Jin, DONG Bowen, LIU Miao, et al. Two-person collaborative brain-controlled robotic arm system for writing Chinese character using P300 and SSVEP features[J]. Journal of Data Acquisition and Processing, 2022, 37(6): 1401–1411. doi: 10.16337/j.1004-9037.2022.06.020
[47]	EDELMAN B J, MENG Jianjun, SUMA D, et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control[J]. Science Robotics, 2019, 4(31): eaaw6844. doi: 10.1126/scirobotics.aaw6844
[48]	KOBLER R J, SBURLEA A I, and MÜLLER-PUTZ G R. Tuning characteristics of low-frequency EEG to positions and velocities in visuomotor and oculomotor tracking tasks[J]. Scientific Reports, 2018, 8(1): 17713. doi: 10.1038/s41598-018-36326-y
[49]	WALDERT S, PISTOHL T, BRAUN C, et al. A review on directional information in neural signals for brain-machine interfaces[J]. Journal of Physiology-Paris, 2009, 103(3/5): 244–254. doi: 10.1016/j.jphysparis.2009.08.007
[50]	HONG Jian and PARK J H. Efficacy of neuro-feedback training for PTSD symptoms: A systematic review and meta-analysis[J]. International Journal of Environmental Research And Public Health, 2022, 19(20): 13096. doi: 10.3390/IJERPH192013096
[51]	BIASIUCCI A, LEEB R, ITURRATE I, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke[J]. Nature Communications, 2018, 9(1): 2421. doi: 10.1038/s41467-018-04673-z
[52]	BARSOTTI M, LEONARDIS D, LOCONSOLE C, et al. A full upper limb robotic exoskeleton for reaching and grasping rehabilitation triggered by MI-BCI[C]. 2015 IEEE International Conference on Rehabilitation Robotics, Singapore, 2015: 49–54.
[53]	LIU Jingyi, ABD-EL-BARR M, and CHI J H. Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients[J]. Neurosurgery, 2016, 79(6): N13–N14. doi: 10.1038/srep30383
[54]	QUANDT F and HUMMEL F C. The influence of functional electrical stimulation on hand motor recovery in stroke patients: A review[J]. Experimental & Translational Stroke Medicine, 2014, 6: 9. doi: 10.1186/2040-7378-6-9
[55]	YOO S S, LEE J H, O'LEARY H, et al. Neurofeedback fMRI-mediated learning and consolidation of regional brain activation during motor imagery[J]. International Journal of Imaging Systems and Technology, 2008, 18(1): 69–78. doi: 10.1002/ima.20139
[56]	XU Minpengg, HE Feng, JUNG T P, et al. Current challenges for the practical application of electroencephalography-based brain-computer interfaces[J]. Engineering, 2021, 7(12): 1710–1712. doi: 10.1016/j.eng.2021.09.011
[57]	LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539
[58]	XU Lichao, XU Minpeng, MA Zhen, et al. Enhancing transfer performance across datasets for brain-computer interfaces using a combination of alignment strategies and adaptive batch normalization[J]. Journal of Neural Engineering, 2021, 18(4): 0460e5. doi: 10.1088/1741-2552/AC1ED2