Individualized channel-selection strategy-based brain-machine interaction rehabilitation training: A pilot study of clinical experiments
JIA Tianyu1, QIAN Chao1, LI Chong1, JI Linhong1, LIU Aixian2, FANG Boyan2
1. Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China; 2. Beijing Rehabilitation Hospital of Capital Medical University, Beijing 100144, China
Abstract：Brain-machine interactions (BMI) can recognize motor intentions and transfer the intentions to control a rehabilitation robot assisting paralyzed limb movement, which can effectively stimulate the neural pathways for motor control and sensory feedback. Lesion types, lesion degrees and lesion sites vary from patient to patient. Motor intention expression may then shift to different brain regions depending on the lesion characteristics. This study presents an individualized channel-selection strategy-based BMI rehabilitation training method which uses the individualized motor expression encephalic area as the electroencephalogram (EEG) collection site. Three stroke patients were recruited for this study and were trained for 10 days using individualized channel-selection strategy-based BMI rehabilitation training. All the patients were assessed using a clinical evaluation scale and EEG tests before and after the 10 day training. The results show that after the training the subjects have improved Fugl-Meyer assessments and one of the subjects regains motor evoked potential which indicates the activation of the neural pathway. This individualized channel-selection strategy-based BMI rehabilitation training method should be further validated with a large number of stroke patients in the future.
贾天宇, 钱超, 李翀, 季林红, 刘爱贤, 方伯言. 基于个性化导联选择策略的脑机交互康复训练临床初步研究[J]. 清华大学学报（自然科学版）, 2020, 60(11): 920-926.
JIA Tianyu, QIAN Chao, LI Chong, JI Linhong, LIU Aixian, FANG Boyan. Individualized channel-selection strategy-based brain-machine interaction rehabilitation training: A pilot study of clinical experiments. Journal of Tsinghua University(Science and Technology), 2020, 60(11): 920-926.
[1] WARRAICH Z, KLEIM J A. Neural plasticity:The biological substrate for neurorehabilitation[J]. PM&R, 2010, 2(12):S208-S219. [2] CHAUDHARY U, BIRBAUMER N, RAMOS- MURGUIALDAY A. Brain-computer interfaces for communication and rehabilitation[J]. Nature Reviews Neurology, 2016, 12(9):513-525. [3] ONO T, SHINDO K, KAWASHIMA K, et al. Brain- computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke[J]. Frontiers in Neuroengineering, 2014, 7:19. [4] RAMOS-MURGUIALDAY A, BROETZ D, REA M, et al. Brain-machine interface in chronic stroke rehabilitation:A controlled study[J]. Annals of Neurology, 2013, 74(1):100-108. [5] ANG K K, GUAN C, PHUA K S, et al. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation:Results of a three-armed randomized controlled trial for chronic stroke[J]. Frontiers in Neuroengineering, 2014, 7:30. [6] ONO T, TOMITA Y, INOSE M, et al. Multimodal sensory feedback associated with motor attempts alters BOLD responses to paralyzed hand movement in chronic stroke patients[J]. Brain Topography, 2015, 28(2):340-351. [7] TAM W K, TONG K Y, MENG F, et al. A minimal set of electrodes for motor imagery BCI to control an assistive device in chronic stroke subjects:A multi-session study[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2011, 19(6):617-627. [8] FRISOLI A, LOCONSOLE C, LEONARDIS D, et al. A new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 2012, 42(6):1169-1179. [9] ZHANG L, ZHANG C, HIGASHI H, et al. Common spatial pattern using multivariate EMD for EEG classification [C]//Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011. Xi'an, China, 2011:244-248. [10] FEYDY A, CARLIER R, ROBYBRAMI A, et al. Longitudinal study of motor recovery after stroke recruitment and focusing of brain activation[J]. Stroke:A Journal of Cerebral Circulation, 2002, 33(6):1610-1617. [11] LI C, JIA T, XU Q, et al. Brain-computer interfacechannel-selection strategy based onanalysis of event-related desynchronization topography in stroke patients[J]. Journal of Healthcare Engineering, 2019, 2019:3817124. [12] LOTZE M, MARKERT J, SAUSENG P, et al. The role of multiple contralesional motor areas for complex hand movements after internal capsular lesion[J]. The Journal of Neuroscience, 2006, 26(22):6096-6102. [13] BIERNASKIE J, SZYMANSKA A, WINDLE V, et al. Bi-hemispheric contribution to functional motor recovery of the affected forelimb following focal ischemic brain injury in rats[J]. European Journal of Neuroscience, 2005, 21(4):989-999. [14] ROSEBOOM W, LINARES D, NISHIDA S Y. Sensory adaptation for timing perception[J]. Proceedings of the Royal Society B:Biological Sciences, 2015, 282(1805):20142833. [15] DELORME A, MAKEIG S. EEGLAB:An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[J]. Journal of Neuroscience Methods, 2004, 134(1):9-21. [16] PFURTSCHELLER G, LOPES DA SILVA F H. Event-related EEG/MEG synchronization and desynchronization:Basic principles[J]. Clinical Neurophysiology, 1999, 110(11):1842-1857.
2020, Vol. 60 
