[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125073-en":3,"doc-seo-125073-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125073,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning-and Deep Learning-Based Myoelectric Control System for Upper Limb Rehabilitation Utilizing EEG and EMG Signals - A Systematic Review","Upper-limb disabilities, often caused by stroke or neurological disorders, severely restrict performance of everyday activities and reduce quality of life. Effective rehabilitation technologies are essential for restoring motor function and improving patient outcomes. This systematic review evaluates how machine learning and deep learning methods are used in myoelectric-controlled upper-limb rehabilitation, emphasizing EEG and EMG signals. Non-invasive acquisition paired with advanced models is assessed for improved control accuracy and efficiency. A search covering January 2015 to July 2024 selected fourteen eligible studies, highlighting gains from LSTM, SVM, and CNN approaches while also addressing robustness, computational demands, and real-time feasibility.","Systematic Review  \nMachine Learning-and Deep Learning-Based Myoelectric Control System for Upper Limb Rehabilitation Utilizing EEG and EMG Signals: A Systematic Review  \nTala Zaim 1, Sara Abdel-Hadi 1, Rana Mahmoud 1, Amith Khandakar 1, Seyed Mehdi Rakhtala 2, * and Muhammad E. H. Chowdhury 1, *  \nAcademic Editor: Dante Mantini  \nReceived: 11 November 2024  \nRevised: 24 December 2024  \nAccepted: 13 January 2025  \nPublished: 3 February 2025  \nCitation: Zaim, T.; Abdel-Hadi, S.; Mahmoud, R.; Khandakar, A.;  \nRakhtala, S.M.; Chowdhury, M.E.H. Machine Learning-and Deep Learning-Based Myoelectric Control System for Upper Limb Rehabilitation Utilizing EEGandEMG Signals: A Systematic Review. Bioengineering 2025, 12, 144. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/bioengineering12020144](10.3390/bioengineering12020144)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; [tz2104202@qu.edu.qa](tz2104202@qu.edu.qa) (T.Z.); [sa2001791@qu.edu.qa](sa2001791@qu.edu.qa) (S.A.-H.); [rm2005272@qu.edu.qa](rm2005272@qu.edu.qa) (R.M.); [amitk@qu.edu.qa](amitk@qu.edu.qa) (A.K.)  \n2 School of Engineering, University of the West of England, Bristol BS16 1QY, UK  \n* [Correspondence: mehdi.rakhtalarostami@uwe.ac.uk](Correspondence: mehdi.rakhtalarostami@uwe.ac.uk) (S.M.R.); [mchowdhury@qu.edu.qa](mchowdhury@qu.edu.qa) (M.E.H.C.) Abstract: Upper limb disabilities, often caused by conditions such as stroke or neurological disorders, severely limit an individual’s ability to perform essential daily tasks, leading to a significant reduction in quality of life. The development of effective rehabilitation technologies is crucial to restoring motor function and improving patient outcomes. This systematic review examines the application of machine learning and deep learning techniques in myoelectric-controlled systems for upper limb rehabilitation, focusing on the use of electroencephalography and electromyography signals. By integrating non-invasive signal acquisition methods with advanced computational models, the review highlightshow these technologies can enhance the accuracy and efficiency of rehabilitation devices. A comprehensive search of literature published between January 2015 and July 2024 led to the selection of fourteen studies that met the inclusion criteria. These studies showcase various approaches in decoding motor intentions and controlling assistive devices, with models such as Long Short-Term Memory Networks, Support Vector Machines, and Convolutional Neural Networks showing notable improvements in control precision. However, challenges remain in terms of model robustness, computational complexity, and real-time applicability. This systematic review aims to provide researchers with a deeper understanding of the current advancements and challenges in this field, guiding future research efforts to overcome these barriers and facilitate the transition of these technologies from experimental settings to practical, real-world applications.  \nKeywords: EMG; EEG; machine learning; deep learning; upper limb; arm; disability; disabilities  \n1. Introduction  \nMotor impairments frequently affect individuals with neuromuscular conditions, including stroke, spinal cord injury, and muscular dystrophy, as well as those with neurodegenerative diseases like multiple sclerosis and amyotrophic lateral sclerosis [1,2] . These disabilities can significantly hinder an individual’s ability to perform essential tasks including mobility and self-care, thereby impacting their overall quality of life [3] . This highlights the critical need for rehabilitation to reduce ad","cbCailXIsPigqdTS","https://ap.wps.com/l/cbCailXIsPigqdTS","pdf",2670670,1,24,"English","en",105,"# Abstract\n# Introduction\n## Upper-limb disabilities and rehabilitation needs\n## Myoelectric control and the role of sEMG\n## EEG as an additional control signal\n## Invasive vs non-invasive signal acquisition","[{\"question\":\"What is the main focus of this systematic review?\",\"answer\":\"It reviews how machine learning and deep learning techniques are applied to myoelectric-controlled systems for upper-limb rehabilitation, with emphasis on EEG and EMG signals.\"},{\"question\":\"Which types of signals are considered for myoelectric control?\",\"answer\":\"The review centers on non-invasive EEG and surface EMG (sEMG), while also discussing the contrast with invasive neural signals and their trade-offs.\"},{\"question\":\"What benefits and remaining challenges are reported across the selected studies?\",\"answer\":\" Studies show improved control precision using models such as LSTM, SVM, and CNN, while challenges include model robustness, computational complexity, and suitability for real-time use.\"}]","Machine Learning-and Deep Learning-Based Myoelectric Control System for Upper Limb Rehabilitation Utilizing EEG and EMG Signals - A Systematic Review | PDF",1785896479,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-deep-learning-based-myoelectric-control-system-for-upper-limb-rehabilitation-utilizing-eeg-and-emg-signals-a-systematic-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-deep-learning-based-myoelectric-control-system-for-upper-limb-rehabilitation-utilizing-eeg-and-emg-signals-a-systematic-review/125073/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main focus of this systematic review?","Question",{"text":75,"@type":76},"It reviews how machine learning and deep learning techniques are applied to myoelectric-controlled systems for upper-limb rehabilitation, with emphasis on EEG and EMG signals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which types of signals are considered for myoelectric control?",{"text":80,"@type":76},"The review centers on non-invasive EEG and surface EMG (sEMG), while also discussing the contrast with invasive neural signals and their trade-offs.",{"name":82,"@type":73,"acceptedAnswer":83},"What benefits and remaining challenges are reported across the selected studies?",{"text":84,"@type":76},"Studies show improved control precision using models such as LSTM, SVM, and CNN, while challenges include model robustness, computational complexity, and suitability for real-time use.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]