Construction of Power Assistive System for the Control of Upper Limb Wearable Exoskeleton Robot with Electroencephalography Signals

被引：0

作者：

Liang, Hongbo ^{[1
]}

Zhu, Chi ^{[1
]}

Tian, Ye ^{[1
]}

Iwata, Yu ^{[1
]}

Maedono, Shota ^{[1
]}

Yu, Haoyong ^{[2
]}

Yan, Yuling ^{[3
]}

Duan, Feng ^{[4
]}

机构：

[1] Maebashi Inst Technol, Dept Environm & Life Engn, Maebashi, Gunma, Japan

[2] Natl Univ Singapore, Fac Engn, Dept Bioengn, Singapore, Singapore

[3] Santa Clara Univ, Sch Engn, Dept Bioengn, Santa Clara, CA 95053 USA

[4] Nankai Univ, Coll Informat Tech Sci, Dept Automat, Tianjin, Peoples R China

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON CYBORG AND BIONIC SYSTEMS (CBS) | 2017年

关键词：

Brain-machine Interface; Motion Estimation; Power Assistive System; Shoulder Joint; Wearable Exoskeleton Robot;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled people and for augmenting human performance. In this work, we propose a motion estimation method based on electroencephalography (EEG) signals to realize the power assistance. In order to improve the accuracy of online estimation, time lag is introduced, and in particular, a linear model that correlates the EMG signal to the EEG signals is constructed based on motion-related features extracted from multi-location EEG signal measurements. The constructed model is used to estimate the human muscular activity of shoulder joint from EEG signals. The proposed approach is experimentally verified. Our results suggest that the estimation of EMG signal based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities.

引用

页码：165 / 168

页数：4