EEG and eye-EMG Combined Control of Rehabilitation Wheelchair Using an Improved Genetic Algorithm

Author:

sun aixi1,yang yujun1,Shan Jun1,ding rui1,Liu Yiding1,li jianping1,zhang yu1

Affiliation:

1. Zhejiang Normal University

Abstract

Abstract

In this paper, we present a combined control system for wheelchairs based on bioelectricity sensors, aimed at enhancing the mobility range of individuals with rehabilitation patients. The approach leverages the capabilities of bioelectricity sensors to read both the action bioelectricity signals of the accessory eye organs (AEO) and mental power level, i.e. an EEG signal and an eye-EMG signal, providing an innovative solution for enhancing the control mechanism of wheelchairs, thereby improving mobility and independence for individuals with movement disorders. The system achieves this by comparing the sample library established ahead, processing the action bioelectricity signals of AEO, and converting them into combined control instructions for the wheelchair. By integrating it with the mental power level and obtaining multiple control instructions, the system's stability is significantly improved. This system enables the wheelchair to perform various movements such as left-turning, right-turning, forward moving, stopping, accelerating, and decelerating. Additionally, the control stability of wheelchair movements is enhanced. To optimize the sample library of AEO action signals, we employ a genetic algorithm that utilizes roulette selection with random acceptance to increase convergence speed. The individual fitness of the population is improved through parent crossover and sorting differential mutation operators. By optimizing the sample library based on bioelectricity sensors, the action signals are classified using eigenvalues, resulting in a further improvement in classification accuracy. The performance of the combined control system is evaluated by utilizing metrics such as accuracy rate, false activation rate, and misjudgment rate. The experimental results validate the excellent performance of the system. The wheelchair was successfully controlled to move towards the destination along a predetermined path using the combined control system. Overall, the combined control system expands the range of activities for rehabilitation patients.

Publisher

Springer Science and Business Media LLC

Reference44 articles.

1. An asynchronous p300 bci with ssvep-based control state detection;Panicker R;IEEE Trans Biomed Eng,2011

2. A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface;Singh A;Sensors,2021

3. Progress in Brain Computer Interface: Challenges and Opportunities;Saha S;Front Syst Neurosci,2021

4. Sellers EW. Clinical applications of brain–computer interface technology. Clin EEG Neurosci 2011];42(4):iv–v.

5. A comprehensive review of EEG-based brain-computer interface paradigms;Abiri R;J Neural Eng,2019

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3