Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton.

Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton.
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为下肢外骨骼开发基于运动图像的实时异步混合 BCI 控制器。

DOI:
10.3390/s20247309
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发表时间:
2020-12-19
期刊:
Sensors (Basel, Switzerland)
影响因子:
--
通讯作者:
Kim H
Kim H
中科院分区:
其他
文献类型:
--
作者:
Choi J;Kim KT;Jeong JH;Kim L;Lee SJ;Kim H

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本研究旨在开发一种基于直觉步态相关运动想象(MI)的下肢外骨骼混合脑-机接口(BCI)控制器,并研究该控制器在站立、前走和坐下等实际场景下的可行性。一个滤波器组共同的空间模式(FBCSP)和基于互信息的最佳个体特征(MIBIF)的选择被用于在研究中解码MI脑电图(EEG)信号,并提取一个特征矩阵作为输入的支持向量机(SVM)分类器。连续眨眼开关顺序地与EEG解码器组合以操作下肢外骨骼。10名受试者在离线(训练)和在线时的准确率均超过80%。所有受试者通过开发的实时BCI控制器穿戴下肢外骨骼成功完成步态任务。与手动智能手表控制器相比,BCI控制器实现了1.45的时间比。开发的系统可能会使患有神经系统疾病的人受益,这些人可能难以操作手动控制。
This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.
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