[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127092-en":3,"doc-seo-127092-105":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},127092,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Effective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning","This study examines how electroencephalography (EEG) can characterize emotions in emotion-evoking virtual reality (VR) settings and evaluates agreement between self-reported emotional states and machine learning predictions. Thirty participants experienced five VR environments targeting distinct emotions, while EEG data were recorded. Participants rated ground-truth valence and arousal using the Self-Assessment Manikin. A Gradient Boosted Decision Tree classifier tested EEG feasibility, revealing neural activation patterns linked to valence and arousal levels and a meaningful correspondence with self-assessments, supporting EEG-based affective indicators as valid ground-truth measures.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nEffective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning  \nOriginal  \nEffective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning / Castiblanco Jimenez, Ivonne Angelica; Olivetti, Elena Carlotta; Vezzetti, Enrico; Moos, Sandro; Celeghin, Alessia; Marcolin, Federica. -In: NEURAL COMPUTING & APPLICATIONS. -ISSN 0941-0643. -ELETTRONICO. -36:35(2024), pp. 22245-22263. [10 . 1007/s00521-024-10240-z]  \nAvailability:  \nThis version is available at: 11583/2996186 since: 2025-01-03T17:29:04Z  \nPublisher:  \nSpringer  \nPublished  \nDOI:10.1007/s00521-024-10240-z  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n17 February 2025  \nNeural Computing and Applications (2024) 36:22245–22263  \n[https://doi.org/10.1007/s00521-024-10240-z](https://doi.org/10.1007/s00521-024-10240-z)  \nEffective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning  \nIvonne Angelica Castiblanco Jimenez 1  • Elena Carlotta Olivetti1 • Enrico Vezzetti1 • Sandro Moos 1 • Alessia Celeghin2 • Federica Marcolin 1  \nReceived: 15 June 2023 /Accepted: 12 July 2024/Published online: 19 September 2024  \n􀀂 The Author(s) 2024  \nAbstract  \nThis study investigates the use of electroencephalography (EEG) to characterize emotions and provides insights into the consistency between self-reported and machine learning outcomes. Thirty participants engaged in five virtual reality environments designed to elicit specific emotions, while their brain activity was recorded. The participants self-assessed their ground truth emotional state in terms of Arousal and Valence through a Self-Assessment Manikin. Gradient Boosted Decision Tree was adopted as a classification algorithm to test the EEG feasibility in the characterization of emotional states. Distinctive patterns of neural activation corresponding to different levels of Valence and Arousal emerged, and a noteworthy correspondence between the outcomes of the self-assessments and the classifier suggested that EEG-based affective indicators can be successfully applied in emotional characterization, shedding light on the possibility of using them as ground truth measurements. These findings provide compelling evidence for the validity of EEG as a tool for emotion characterization and its contribution to a better understanding of emotional activation.  \nKeywords Emotion assessment 􀀂 EEG 􀀂 VR 􀀂 Affective elicitation  \n& Ivonne Angelica Castiblanco Jimenez ivonne.castiblanco@polito.it  \nElena Carlotta Olivetti  \nelena.olivetti@polito.it  \nEnrico Vezzetti  \nenrico.vezzetti@polito.it  \nSandro Moos  \nsandro.moos@polito.it  \nAlessia Celeghin  \nalessia.celeghin@unito.it  \nFederica Marcolin  \nfederica.marcolin@polito.it  \n1 Department of Management and Production Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24,  \n10129 Turin, Italy  \n2 Department of Psychology, Universit`a degli Studi di Torino, Via Verdi, 8, 10124 Turin, Italy  \n1 Introduction  \nEmotions are dynamic multidimensional responses that integrate physiological, psychological, and cognitive processes, triggered by internal and external stimuli [1] . Indeed, they are involved in logical decision-making, perception, human interaction, and human intelligence [2, 3] . In recent years, affective computing has emerged as a multidisciplinary sector considering psychology, physiology, computer sciences, and biomedical engineering [4] . As a result, the research and development of systems and interfaces capable of recognizing and emulating human emotions are increasingly becoming a priority [4–6] . In this regard, affective computing can deal with both emotional classification and elicitation, offering significant societal benefits","cbCaivIuKkVsgaLe","https://ap.wps.com/l/cbCaivIuKkVsgaLe","pdf",2200462,1,20,"English","en",105,"# Abstract\n# Introduction\n## Emotions and affective computing\n## Self-reports and SAM/AVS model\n## EEG signals for valence and arousal","[{\"question\":\"How were emotions evoked and labeled in the study?\",\"answer\":\"Thirty participants went through five VR environments designed to elicit specific emotions. Ground-truth valence and arousal were collected via the Self-Assessment Manikin (SAM).\"},{\"question\":\"Which machine learning method was used to classify emotional states?\",\"answer\":\"The study used a Gradient Boosted Decision Tree classifier to test the feasibility of EEG signals for characterizing emotional states.\"},{\"question\":\"What relationship was observed between EEG-based predictions and self-reports?\",\"answer\":\"Distinct EEG activation patterns emerged for different valence and arousal levels, and results showed a noteworthy correspondence between self-assessment outcomes and classifier predictions.\"}]","Effective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning | PDF",1785936808,50,{"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},"effective-affective-eeg-based-indicators-in-emotion-evoking-vr-environments-an-evidence-from-machine-learning","",{"@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/effective-affective-eeg-based-indicators-in-emotion-evoking-vr-environments-an-evidence-from-machine-learning/127092/",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},"How were emotions evoked and labeled in the study?","Question",{"text":75,"@type":76},"Thirty participants went through five VR environments designed to elicit specific emotions. Ground-truth valence and arousal were collected via the Self-Assessment Manikin (SAM).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning method was used to classify emotional states?",{"text":80,"@type":76},"The study used a Gradient Boosted Decision Tree classifier to test the feasibility of EEG signals for characterizing emotional states.",{"name":82,"@type":73,"acceptedAnswer":83},"What relationship was observed between EEG-based predictions and self-reports?",{"text":84,"@type":76},"Distinct EEG activation patterns emerged for different valence and arousal levels, and results showed a noteworthy correspondence between self-assessment outcomes and classifier predictions.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","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":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]