[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126857-en":3,"doc-seo-126857-105":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},126857,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Analyzing Brain Activity During Learning Tasks with EEG and Machine Learning - Research report","Analyzing brain activity during STEM learning tasks uses EEG signals to examine whether different cognitive activities can be classified from neural data. EEG data from twenty subjects performing five cognitive tasks were segmented into 4-second clips and transformed into power spectral density features across brain frequency bands. Models including XGBoost, Random Forest, and Bagging Classifier were tested across k-interval settings, with Random Forest reaching 91.07% testing accuracy at interval size two. Using all four channels highlighted cognitive flexibility, while lobe-specific results linked right frontal regions to mathematical planning and left regions to flexibility and connections. The findings support machine learning for interpreting learning mechanisms.","Analyzing Brain Activity During Learning Tasks with EEG and Machine Learning  \nRyan Cho1, Mobasshira Zaman2, Kyu Taek Cho3, Jaejin Hwang2*  \n1Illinois Mathematics and Science Academy, Aurora, IL  \n2Department of Industrial and Systems Engineering, Northern Illinois University, DeKalb, IL 3Mechanical Engineering Department, Northern Illinois University, DeKalb, IL.  \nCorresponding author’s Email: [j](jhwang3@niu.edu)[hwang3@niu.edu](jhwang3@niu.edu)  \nAbstract  \nThis study aimed to analyze brain activity during various STEM activities, exploring the feasibility of classifying between different tasks. EEG brain data from twenty subjects engaged in five cognitive tasks were collected and segmented into 4-second clips. Power spectral densities of brain frequency waves were then analyzed. Testing different k-intervals with XGBoost, Random Forest, and Bagging Classifier revealed that Random Forest performed best, achieving a testing accuracy of 91.07% at an interval size of two. When utilizing all four EEG channels, cognitive flexibility was most recognizable. Task-specific classification accuracy showed the right frontal lobe excelled in mathematical processing and planning, the left frontal lobe in cognitive flexibility and mental flexibility, and the left temporoparietal lobe in connections. Notably, numerous connections between frontal and temporoparietal lobes were observed during STEM activities. This study contributes to a deeper understanding of implementing machine learning in analyzing brain activity and sheds light on the brain's mechanisms.  \nKeywords: machine learning, electroencephalography, cognitive neuroscience, education  \n1. Introduction  \nRecent statistics have highlighted the rapid regression of students’math and reading skills to the worst levels (Mervosh, 2022). Amidst this massive decrease in education, this research explores ifEEG sensors can be used as a teaching tool to help increase education rates. If teachers could monitor student learning patterns, they would have the ability to restructure lectures to be more engaging and adapt to the specific thinking processes that students prefer to use. Not to mention, it could also help teachers understand if problems are too difficult or easy to understand from students’ Zone of Proximal Development (Vygotsky, 1978) .  \nSTEM (Science, Technology, Engineering, and Mathematics) learning encompasses a wide range of thinking methods for various types of activities. In this study, ‘working memory ’, ‘planning ’, ‘arithmetic functioning ’, ‘mental flexibility’, and ‘cognitive flexibility ’ were analyzed in detail. Working memory is crucial for tasks such as reasoning, verbal comprehension, and mathematical skills (Forsberg et al., 2021) . Planning involves creating new learning strategies to approach a problem (Han et al., 2021) . Arithmetic functioning involves understanding numerical information and performing calculations (Li, Wang, et al., 2020) . Mental flexibility is the ability to generate original ideas and adapt to different situations (Elsayed & Abdo, 2022) . Cognitive flexibility is the ability to switch between or think about multiple concepts simultaneously, making one look at multiple different angles of a problem (Cheng & Koszalka, 2016) . While all these activities seem very similar, they create distinct patterns in the brain.  \nEach of these cognitive abilities represents different learning styles that students may prefer over others. These different preferences lead to different levels of success in the classroom depending on the learning style being used (M. Wilson, 2012). Recognizing these differences can help educators tailor their teaching strategies to meet the diverse needs of their students in the STEM fields (İlçin et al., 2018) .  \nTraditional testing methods, such as quizzes, may not fully capture a student’s real-time understanding and learning process. These assessments often focus on the product of learning rather than the process of thi","cbCaipS2NyTEsrAa","https://ap.wps.com/l/cbCaipS2NyTEsrAa","pdf",667293,1,20,"English","en",105,"# Introduction\n## Motivation from student skill decline\n## Cognitive tasks and learning styles\n## Limits of traditional assessments\n## EEG and machine learning as teaching tools\n# Related work (EEG and learning)","[{\"question\":\"What data and tasks were used to analyze brain activity?\",\"answer\":\"EEG brain data were collected from twenty subjects who performed five cognitive tasks related to STEM learning. The signals were segmented into 4-second clips for analysis.\"},{\"question\":\"Which machine learning model performed best, and what accuracy was achieved?\",\"answer\":\"Random Forest performed best, achieving 91.07% testing accuracy when the interval size was set to two.\"},{\"question\":\"How did brain regions relate to different cognitive functions in the results?\",\"answer\":\"The study reports right frontal lobe strength for mathematical processing and planning, left frontal lobe strength for cognitive flexibility and mental flexibility, and left temporoparietal lobe prominence for connections. Numerous frontal–temporoparietal connections appeared during STEM activities.\"}]","Analyzing Brain Activity During Learning Tasks with EEG and Machine Learning - Research report | PDF",1785935258,50,{"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},"analyzing-brain-activity-during-learning-tasks-with-eeg-and-machine-learning-research-report","",{"@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/analyzing-brain-activity-during-learning-tasks-with-eeg-and-machine-learning-research-report/126857/",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 data and tasks were used to analyze brain activity?","Question",{"text":75,"@type":76},"EEG brain data were collected from twenty subjects who performed five cognitive tasks related to STEM learning. The signals were segmented into 4-second clips for analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning model performed best, and what accuracy was achieved?",{"text":80,"@type":76},"Random Forest performed best, achieving 91.07% testing accuracy when the interval size was set to two.",{"name":82,"@type":73,"acceptedAnswer":83},"How did brain regions relate to different cognitive functions in the results?",{"text":84,"@type":76},"The study reports right frontal lobe strength for mathematical processing and planning, left frontal lobe strength for cognitive flexibility and mental flexibility, and left temporoparietal lobe prominence for connections. 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