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Bioengineering 2019, 6(1), 2; https://doi.org/10.3390/bioengineering6010002

Feature Extraction of Shoulder Joint’s Voluntary Flexion-Extension Movement Based on Electroencephalography Signals for Power Assistance

1
Department of Environment and Life Engineering, Graduate School of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan
2
Department of Environment and Life Engineering and Department of Systems Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan
3
Department of Systems Life Engineering, Graduate School of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan
4
Department of Systems Life Engineering, Faculty of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan
5
Department of Bioengineering, Faculty of Engineering, National University of Singapore, Singapore 119077, Singapore
6
Department of Bioengineering, School of Engineering, Santa Clara University, Santa Clara, CA 1304, USA
7
Department of Automation, College of Information Technical Science, Nankai University, Tianjin 300071, China
*
Author to whom correspondence should be addressed.
Received: 16 November 2018 / Revised: 19 December 2018 / Accepted: 20 December 2018 / Published: 24 December 2018
(This article belongs to the Special Issue Biosignal Processing)
Full-Text   |   PDF [15622 KB, uploaded 24 December 2018]   |  

Abstract

Brain-Machine Interface (BMI) has been considered as an effective way to help and support both the disabled rehabilitation and healthy individuals’ daily lives to use their brain activity information instead of their bodies. In order to reduce costs and control exoskeleton robots better, we aim to estimate the necessary torque information for a subject from his/her electroencephalography (EEG) signals when using an exoskeleton robot to perform the power assistance of the upper limb without using external torque sensors nor electromyography (EMG) sensors. In this paper, we focus on extracting the motion-relevant EEG signals’ features of the shoulder joint, which is the most complex joint in the human’s body, to construct a power assistance system using wearable upper limb exoskeleton robots with BMI technology. We extract the characteristic EEG signals when the shoulder joint is doing flexion and extension movement freely which are the main motions of the shoulder joint needed to be assisted. Independent component analysis (ICA) is used to extract the source information of neural components, and then the average method is used to extract the characteristic signals that are fundamental to achieve the control. The proposed approach has been experimentally verified. The results show that EEG signals begin to increase at 300–400 ms before the motion and then decrease at the beginning of the generation of EMG signals, and the peaks appear at about one second after the motion. At the same time, we also confirmed the relationship between the change of EMG signals and the EEG signals on the time dimension, and these results also provide a theoretical basis for the delay parameter in the linear model which will be used to estimate the necessary torque information in future. Our results suggest that the estimation of torque information 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 continuously. View Full-Text
Keywords: brain-machine interface; power assistance system; feature extraction; shoulder joint brain-machine interface; power assistance system; feature extraction; shoulder joint
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Liang, H.; Zhu, C.; Iwata, Y.; Maedono, S.; Mochita, M.; Liu, C.; Ueda, N.; Li, P.; Yu, H.; Yan, Y.; Duan, F. Feature Extraction of Shoulder Joint’s Voluntary Flexion-Extension Movement Based on Electroencephalography Signals for Power Assistance. Bioengineering 2019, 6, 2.

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