[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121689-en":3,"doc-seo-121689-105":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},121689,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",7,"Healthcare","Mental State Prediction Using Machine Learning and EEG Signal - BCI Mental Health Classification","Brain-computer interface (BCI) research enables data exchange between neural activity and electronic devices, supporting healthcare and neuroergonomics. This work classifies EEG brain-wave signals to predict mental health states using machine learning models. Results show XGBoost achieves 99.62% accuracy, outperforming other comparable studies for diagnosing mental states from EEG patterns. The findings support advancing EEG-based mental state prediction toward practical clinical and supportive applications.","Mental State Prediction Using Machine Learning and  \nEEG Signal  \nDinesh Datar1 , Dr. R N Khobragade2  \n1Department of Computer Science & Engineering  \nSant Gadge Baba Amravati University,  \nAmravati, India  \n[dineshdatar@gmail.com](dineshdatar@gmail.com)  \n2Department of Computer Science & Engineering  \nSant Gadge Baba Amravati University,  \nAmravati, India  \n[rnkhobragade@gmail.com](rnkhobragade@gmail.com)  \nAbstract – One of the most exciting areas of computer science right now is brain-computer interface (BCI) research. A conduit for data flow between both the brain as well as an electronic device is the brain-computer interface (BCI) . Researchers in several disciplines have benefited from the advancements made possible by brain-computer interfaces. Primary fields of study include healthcare and neuroergonomics. Brain signals could be used in a variety of ways to improve healthcare at every stage, from diagnosis to rehabilitation to eventual restoration. In this research, we demonstrate how to classify EEG signals of brain waves using machine learning algorithms for predicting mental health states. The XGBoost algorithm's results have an accuracy of 99.62%, which is higher than that of any other study of its kind and the best result to date for diagnosing people's mental states from their EEG signals. This discovery will aid in taking efforts [1] to predict mental state using EEG signals to the next level.  \nKeywords : BCI, EEG Signals, Support Vector Machine ,Random Forest , XGBoost.  \nI. INTRODUCTION  \nBCI technology is a potent tool for facilitating communication between people and machines. Issuing commands and completing the interaction does not necessitate any additional hardware or manual effort [1] . BCIs were first developed by the research community for use in the medical field, which then led to the creation of assistive devices [2] . They have made it easier to replace lost motor function and restore mobility for people who are physically disabled or confined [3] .  \nResearchers have been inspired by BCI's bright future to investigate the technology's potential medicinal uses in people's everyday lives.Electrodeplacement on the scalp allows for electroencephalography (EEG), a noninvasive approach for recording the brain's electrical activity. These electrical impulses, or EEG waves, can be characterised by their frequency and amplitude. When a person is sleeping or otherwise at ease, brain waves have a low frequency, however as soon as they begin to react to their surroundings, the waves' frequency rises. More intense waves are generated when one's attention is focused [4] . Several international, interdisciplinary, and collaborative research projects are currently using a wide range of approaches to study the brain and emotional processing [5] . In particular, the gap between human and machine has been narrowed by indications of surface brain activity, which can be used to  \ndiscern mental processes. Electrical activity in the brain takes the form of distinct patterns called \"signals,\" which are formed by the coordinated firing of billions of neurons and reflect a person's current state of mind and actions [6] . When electrodes are implanted on a person's scalp, it's possible to observe and extract voltage patterns noninvasively; this method is called an electroencephalogram (EEG) [7] .  \nHowever, it is difficult for doctors to correctly categorise EEG signals so that they may characterise the various mental states and recommend the most appropriate followup visit. The complicated time-frequency structure of nonlinear, nonstationary signals, which include a range of oscillating patterns, frequency bands, as well as noise components, can be used to identify them (artefacts) [8] . As artefacts, or non-brain-related physiological signals, render EEG signals unpredictable and diminish their clinical utility, they warrant special consideration. Since scalp EEG is a dynamic signal generated by a large number of cor","cbCaimX3C5twfjEL","https://ap.wps.com/l/cbCaimX3C5twfjEL","pdf",350386,1,6,"English","en",105,"# Abstract\n# Introduction\n## Brain-computer interface and EEG fundamentals\n## Challenges in EEG signal classification\n## Role of AI/ML and telemedicine context\n# Related Work","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses predicting mental health states by classifying EEG signals using machine learning algorithms.\"},{\"question\":\"Which machine learning method shows the best performance?\",\"answer\":\"The XGBoost algorithm reports 99.62% accuracy, higher than other studies mentioned.\"},{\"question\":\"Why is EEG-based mental state recognition difficult?\",\"answer\":\"EEG signals have complex nonlinear, nonstationary time-frequency structure and noise/artefacts, which reduces clinical utility and makes classification challenging.\"}]","Mental State Prediction Using Machine Learning and EEG Signal - BCI Mental Health Classification | PDF",1785806257,15,{"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},"mental-state-prediction-using-machine-learning-and-eeg-signal-bci-mental-health-classification","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/mental-state-prediction-using-machine-learning-and-eeg-signal-bci-mental-health-classification/121689/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the document address?","Question",{"text":75,"@type":76},"It addresses predicting mental health states by classifying EEG signals using machine learning algorithms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning method shows the best performance?",{"text":80,"@type":76},"The XGBoost algorithm reports 99.62% accuracy, higher than other studies mentioned.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is EEG-based mental state recognition difficult?",{"text":84,"@type":76},"EEG signals have complex nonlinear, nonstationary time-frequency structure and noise/artefacts, which reduces clinical utility and makes classification challenging.","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},[93,97,101,105,110,114,117,122,127,130,134],{"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":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",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"]