EEG-Based EMG Estimation of Shoulder Joint for the Power Augmentation System of Upper Limbs

Hongbo Liang,Naoya Ueda,Yingxin Yu,Peirang Li,Chang Liu,Chi Zhu,Mika Mochida

doi:10.3390/sym12111851

Abstract

Brain–Machine Interfaces (BMIs) have attracted much attention in recent decades, mainly for their applications involving severely disabled people. Recently, research has been directed at enhancing the ability of healthy people by connecting their brains to external devices. However, there are currently no successful research reports focused on robotic power augmentation using electroencephalography (EEG) signals for the shoulder joint. In this study, a method is proposed to estimate the shoulder’s electromyography (EMG) signals from EEG signals based on the concept of a virtual flexor–extensor muscle. In addition, the EMG signal of the deltoid muscle is used as the virtual EMG signal to establish the EMG estimation model and evaluate the experimental results. Thus, the shoulder’s power can be augmented by estimated virtual EMG signals for the people wearing an EMG-based power augmentation exoskeleton robot. The estimated EMG signal is expressed via a linear combination of the features of EEG signals extracted by Independent Component Analysis, Short-time Fourier Transform, and Principal Component Analysis. The proposed method was verified experimentally, and the average of the estimation correlation coefficient across different subjects was 0.78 (±0.037). These results demonstrate the feasibility and potential of using EEG signals to provide power augmentation through BMI technology.

Highlights

With the rapid aging of the population and the increase in the number of disabled people, the care burden increases as does the demand for power augmentation
Since power augmentation robots require high real-time performance, we focus on the frequency band below 45 Hz, which is different from previous studies that were limited to single low-frequency band data [20,22]
A linear model for EMG estimation is established with Principal Component Analysis (PCA), and the results show that the EMG signal is successfully estimated during voluntary flexion and extension, which had an average r-value of more than 0.78 across three subjects

Summary

Introduction

With the rapid aging of the population and the increase in the number of disabled people, the care burden increases as does the demand for power augmentation. When transferring a patient from a bed to a wheelchair in caregiving or when carrying heavy objects in logistic centers or construction sites, our upper limbs endure a large amount of strain. In such tasks, the most dominant movements of the upper limbs are the flexion and extension of elbow and shoulder joints. Hybrid Assistive Limb (HAL) is an outstanding example of a wearable exoskeleton controlled by EMG signals [2]. This controller allows for the robust and smooth generation of suitable motion patterns for different motion modes. We developed a wearable upper limb exoskeleton robot based on EMG signals and successfully achieved power augmentation [15]

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