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SSVEP-based active control of an upper limb exoskeleton using a low-cost brain–computer interface
Industrial Robot ( IF 1.8 ) Pub Date : 2021-08-24 , DOI: 10.1108/ir-03-2021-0062
Yue Xu 1 , Qingcong Wu 1 , Bai Chen 1 , Xi Chen 1
Affiliation  

Purpose

For the robot-assisted upper limb rehabilitation training process of the elderly with damaged neuromuscular channels and hemiplegic patients, bioelectric signals are added to transform the traditional passive training mode into the active training mode.

Design/methodology/approach

This paper mainly builds a steady-state visual stimulation interface, an electroencephalography (EEG) signal processing platform and an exoskeleton robot verification platform. The target flashing stimulation blocks provide visual stimulation at the specified position according to the specified frequency and stimulate EEG signals of different frequency bands. The EEG signal-processing platform constructed in this paper removes the noise by using Butterworth band-pass filtering and common average reference filtering on the obtained signals. Further, the features are extracted to identify the volunteer’s active movement intention through the canonical correlation analysis (CCA) method. The classification results are transmitted to the upper limb exoskeleton robot control system, combined with the position and posture of the exoskeleton robot to control the joint motion of robot.

Findings

Through a large number of experimental studies, the average accuracy of offline recognition of motion intention recognition can reach 86.1%. The control strategy with a three-instruction judgment method reduces the average execution error rate of the entire control system to 6.75%. Online experiments verify the feasibility of the steady-state visual evoked potentials (SSVEP)-based rehabilitation system.

Originality/value

An EEG signal analysis method based on SSVEP is integrated into the control of an upper limb exoskeleton robot, transforming the traditional passive training mode into the active training mode. The device used to record EEG is of very low cost, which has the potential to promote the rehabilitation system for further widely applications.



中文翻译:

使用低成本脑机接口基于 SSVEP 的上肢外骨骼主动控制

目的

针对神经肌肉通道受损的老年人和偏瘫患者的机器人辅助上肢康复训练过程,加入生物电信号,将传统的被动训练模式转变为主动训练模式。

设计/方法/方法

本文主要搭建了稳态视觉刺激接口、脑电图(EEG)信号处理平台和外骨骼机器人验证平台。目标闪烁刺激块根据指定频率在指定位置提供视觉刺激,刺激不同频段的脑电信号。本文构建的脑电信号处理平台对获得的信号采用巴特沃斯带通滤波和公共平均参考滤波去除噪声。此外,通过典型相关分析(CCA)方法提取特征以识别志愿者的主动运动意图。分类结果传送到上肢外骨骼机器人控制系统,

发现

通过大量的实验研究,运动意图识别的离线识别平均准确率可以达到86.1%。采用三指令判断法的控制策略,将整个控制系统的平均执行错误率降低到6.75%。在线实验验证了基于稳态视觉诱发电位 (SSVEP) 的康复系统的可行性。

原创性/价值

将基于SSVEP的脑电信号分析方法集成到上肢外骨骼机器人的控制中,将传统的被动训练模式转变为主动训练模式。用于记录脑电图的设备成本非常低,具有促进康复系统进一步广泛应用的潜力。

更新日期:2021-08-24
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