[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121671-en":3,"doc-seo-121671-105":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":20,"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},121671,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Trends in Machine Learning and Electroencephalogram (EEG) - A Review for Undergraduate Researchers","This paper presents a systematic literature review of Brain-Computer Interfaces (BCIs) in the context of Machine Learning, with emphasis on Electroencephalography (EEG) research and the latest trends available as of 2023. It aims to give undergraduate researchers an accessible overview of the BCI field, covering representative tasks, machine learning algorithms, and commonly used datasets. By synthesizing recent findings, the study supports foundational understanding and highlights promising directions for future investigations.","arXiv :2307 .028 19v 1 [ cs .HC] 6 Jul 2023  \nTrends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers  \nNathan Koome Murungi 1[0009􀀀0008􀀀3170􀀀7689], Michael Vinh Pham 1[0009􀀀0007􀀀5322􀀀1543], Xufeng Dai2[0009􀀀0006􀀀0513􀀀1255], and Xiaodong  \nQu 1[0000􀀀0001􀀀7610􀀀6475]?  \n1 Swarthmore College, Swarthmore PA 19081, USA  \nfnmurung1,mpham1,[xqu1](xqu1g@swarthmore.edu)[g](xqu1g@swarthmore.edu)[@swarthmore.edu](xqu1g@swarthmore.edu)[ ](xqu1g@swarthmore.edu)2 Haverford College, Haverford, PA 19041, [USA](USA xdai1@haverford.edu)[ xdai1@haverford.edu](USA xdai1@haverford.edu)  \nAbstract. This paper presents a systematic literature review on BrainComputer Interfaces (BCIs) in the context of Machine Learning. Our focus is on Electroencephalography (EEG) research, highlighting the latest trends as of 2023 . The objective is to provide undergraduate researchers with an accessible overview of the BCI 􀀌eld, covering tasks, algorithms, and datasets. By synthesizing recent 􀀌ndings, our aim is to o􀀋er a fundamental understanding of BCI research, identifying promising avenues for future investigations.  \nKeywords: Machine Learning · Deep Learning · Brain-Computer Interfaces · BCI · Electroencephalography · EEG · Undergrad · Review  \n1 Introduction  \nSince the advent of computing, the disparity between human and computer technology has signi􀀌cantly diminished. Starting with early human-computer interfaces like keyboards and microphones, the boundary between humans and computers has been progressively blurred, primarily owing to the emergence and utilization of brain-computer interfaces [34] . In the rapidly advancing 􀀌eld of Brain-Computer Interfaces (BCI), Electroencephalography (EEG) analysis plays a crucial role in establishing a connection between the human brain and Machine Learning (ML) algorithms [5,11,37,40,43,44,45] . The proliferation of ML algorithms and the increasing availability of EEG data have created exciting opportunities for researchers to explore new approaches to interpreting raw EEG data. However, this progress presents a challenge for newcomers due to the overwhelming volume of research papers and the rapid rate at which they become outdated, making it challenging to navigate the research landscape e􀀋ectively.  \nTo tackle this formidable challenge, we present a meticulous examination of the existing literature in the 􀀌eld of Brain-Computer Interfaces (BCI), with a  \n? Nathan, Michael, and Xufeng are the 􀀌rst three authors of this paper, and they contributed equally. Professor Xiaodong Qu is the mentor for this research project.  \n2 N. Murungi et al.  \nspeci􀀌c focus on the most recent advancements up to 2023 . This paper is tailored to cater to undergraduate researchers, serving as a comprehensive overview and guide for those aspiring to conduct research in the Electroencephalography (EEG) domain. By systematically analyzing and organizing the obtained 􀀌ndings, our objective is to facilitate a profound comprehension of the current landscape of BCI research while identifying promising avenues for future investigations.  \nIn addition to providing a comprehensive review, this paper speci􀀌cally delves into utilizing Transformers, a prominent and rapidly emerging machine learning algorithm, within the realm of BCI research. By focusing on the application of Transformers in this context, we aim to shed light on its signi􀀌cance and impact, o􀀋ering insights into its potential advantages and limitations. Table 1 lists the acronyms used in this paper.  \n1.1 Research Questions  \nOur research aims to address the following questions at the intersection of MLand EEG research:  \n{ What are the most suitable tasks, datasets, and ML algorithms for undergraduate researchers to explore the realms of Machine Learning (ML) and Electroencephalography (EEG)?  \n{ What are the prevailing trends observed within the intersection of ML and EEG in 2023?  \nBy answering the 􀀌rst question, we aim to provide guidance ","cbCaisvPb8cGuwhf","https://ap.wps.com/l/cbCaisvPb8cGuwhf","pdf",317183,1,19,"English","en",105,"# Introduction\n## Research Questions\n# Methods\n## Keywords\n# Abbreviations and Acronyms","[{\"question\":\"What is the main focus of the review paper?\",\"answer\":\"The review focuses on Brain-Computer Interfaces (BCIs) and how Brain-Computer Interface research is approached using Machine Learning, emphasizing EEG-based work.\"},{\"question\":\"Which year do the authors use as the reference for “latest trends”?\",\"answer\":\"The paper highlights the latest trends as of 2023 and discusses advancements within the ML–EEG intersection up to that timeframe.\"},{\"question\":\"What kinds of guidance does the paper provide for undergraduate researchers?\",\"answer\":\"It organizes tasks, algorithms, and datasets to help undergraduate researchers identify suitable starting points and resources, and it explains how to interpret the current research landscape.\"}]","Trends in Machine Learning and Electroencephalogram (EEG) - A Review for Undergraduate Researchers | PDF",1785806120,48,{"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},"trends-in-machine-learning-and-electroencephalogram-eeg-a-review-for-undergraduate-researchers","",{"@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/trends-in-machine-learning-and-electroencephalogram-eeg-a-review-for-undergraduate-researchers/121671/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main focus of the review paper?","Question",{"text":75,"@type":76},"The review focuses on Brain-Computer Interfaces (BCIs) and how Brain-Computer Interface research is approached using Machine Learning, emphasizing EEG-based work.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which year do the authors use as the reference for “latest trends”?",{"text":80,"@type":76},"The paper highlights the latest trends as of 2023 and discusses advancements within the ML–EEG intersection up to that timeframe.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of guidance does the paper provide for undergraduate researchers?",{"text":84,"@type":76},"It organizes tasks, algorithms, and datasets to help undergraduate researchers identify suitable starting points and resources, and it explains how to interpret the current research landscape.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]