Motor Imagery Hand Movement Direction Decoding Using Brain Computer Interface to Aid Stroke Recovery and Rehabilitation

脑-机接口 运动表象 脑电图 神经康复 物理医学与康复 冲程(发动机) 康复 会话(web分析) 任务(项目管理) 解码方法 神经解码 心理学 大脑活动与冥想 接口(物质) 神经可塑性 计算机科学 医学 神经科学 机械工程 电信 管理 气泡 最大气泡压力法 并行计算 万维网 工程类 经济
作者
V. K. Benzy,A. P. Vinod,R Subasree,Suvarna Alladi,Raghavendra Kenchaiah
出处
期刊:IEEE Transactions on Neural Systems and Rehabilitation Engineering [Institute of Electrical and Electronics Engineers]
卷期号:28 (12): 3051-3062 被引量:69
标识
DOI:10.1109/tnsre.2020.3039331
摘要

Motor Imagery (MI)-based Brain Computer Interface (BCI) system is a potential technology for active neurorehabilitation of stroke patients by complementing the conventional passive rehabilitation methods. Research to date mainly focused on classifying left vs. right hand/foot MI of stroke patients. Though a very few studies have reported decoding imagined hand movement directions using electroencephalogram (EEG)-based BCI, the experiments were conducted on healthy subjects. Our work analyzes MI-based brain cortical activity from EEG signals and decodes the imagined hand movement directions in stroke patients. The decoded direction (left vs. right) of hand movement imagination is used to provide control commands to a motorized arm support on which patient's affected (paralyzed) arm is placed. This enables the patient to move his/her stroke-affected hand towards the intended (imagined) direction that aids neuroplasticity in the brain. The synchronization measure called Phase Locking Value (PLV), extracted from EEG, is the neuronal signature used to decode the directional movement of the MI task. Event-related desynchronization/synchronization (ERD/ERS) analysis on Mu and Beta frequency bands of EEG is done to select the time bin corresponding to the MI task. The dissimilarities between the two directions of MI tasks are identified by selecting the most significant channel pairs that provided maximum difference in PLV features. The training protocol has an initial calibration session followed by a feedback session with 50 trials of MI task in each session. The feedback session extracts PLV features corresponding to most significant channel pairs which are identified in the calibration session and is used to predict the direction of MI task in left/right direction. An average MI direction classification accuracy of 74.44% is obtained in performing the training protocol and 68.63% from the prediction protocol during feedback session on 16 stroke patients.
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