[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-503132-105":59,"doc-detail-503132-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","exploratory-eeg-analysis-using-clustering-and-phase-locking-factor","Exploratory EEG Analysis Using Clustering and Phase-Locking Factor","","Emotion recognition is essential for psychological and psychiatric applications and to improve human-machine interaction quality. This paper applies clustering algorithms to multiple feature spaces derived from EEG during a stress-inducing task. Features are built from raw EEG, EEG after ICA-based eye-movement artifact removal, and EMD-extracted data-driven modes. From these, band-power features and pairwise phase-locking factors are computed, forming six feature spaces for clustering. Results highlight low-cluster prevalence and consecutive-line cluster structure.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exploratory-eeg-analysis-using-clustering-and-phase-locking-factor/503132/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploratory-eeg-analysis-using-clustering-and-phase-locking-factor/503132.png","ImageObject",300,407,{"name":92,"@type":93},"Fans","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-10-03",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the paper?","Question",{"text":112,"@type":113},"The paper aims to automatically assess a subject’s emotional state using exploratory EEG analysis combined with clustering methods.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are the EEG features constructed for clustering?",{"text":117,"@type":113},"Features come from three processing routes: raw EEG, ICA-processed EEG to reduce eye artifacts, and EMD to extract data-driven modes, from which band-power features and phase-locking factors are computed.",{"name":119,"@type":110,"acceptedAnswer":120},"What key clustering findings are reported?",{"text":121,"@type":113},"Clustering results show interesting patterns, including a tendency toward fewer clusters and clusters that often consist of consecutive test lines.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},503132,1791581728,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Exploratory EEG Analysis Using Clustering and Phase-Locking Factor  \nCarlos Carreiras, Helena Aidos, Hugo Silva and Ana Fred  \nInstituto de Telecomunicac¸ o˜es, Instituto Superior Te´cnico, Lisbon, Portugal  \nfcarlos.carreiras, haidos, hugo.silva, [afred](afredg@lx.it.pt)[g](afredg@lx.it.pt)[@lx.it.pt](afredg@lx.it.pt)  \nKeywords: Emotions, EEG, ICA, EMD, Phase-Locking Factor, Clustering  \nAbstract: Emotion recognition is essential for psychological and psychiatric applications and for improving the quality of human-machine interaction. Therefore, a simple and reliable method is needed to automatically assess the emotional state of a subject. This paper presents an application of clustering algorithms to feature spaces obtained from the acquired EEG of subjects performing a stress-inducing task. These features were obtained in three ways: using the EEG directly, using ICA to remove eye movement artifacts, and using EMD to extract data-driven modes present in the signals. From these features, we computed band-power features (BPFs) as well as pairwise phase-locking factors (PLFs), in a total of six different feature spaces. These six feature spaces are used as input to various clustering algorithms. The results of these clustering techniques show interesting phenomena, including prevalence for low numbers of clusters and the fact that clusters tend to be made of consecutive test lines.  \n1 INTRODUCTION  \nEmotions play a pivotal role in human communication, sometimes even more important than the actual ideas being transmitted. For instance, imagine yourself vacationing in a foreign country, not understanding the local language. Naturally, there is a gaping language barrier in any communication attempt. Nevertheless, you can easily and intuitively gage the emotional state of your interlocutor, knowing if s/he is being aggressive, pleasant, sad, happy, etc., allowing to infer, in rough terms, what is going on. Indeed, the need to communicate emotion has inspired the development of the famous emoticons for text-based Internet chatting, though the participants may not always be truthful (Herbert, 2012) .  \nHowever, this aspect of communication is lacking in human-machine interaction, where the machines (e.g. your personal computer) cannot understand nor produce emotional cues, which could potentially augment the interaction quality. Therefore, there is a need to, by simple and reliable means, assess the emotional state of a person, not only in the context of human-machine interaction, but also in psychological and psychiatric studies, which many times rely on self-evaluation questionnaires to assess the emotions elicited during the experiments (Coan and Allen, 2007) .  \nOne possible approach to automatic emotion  \nrecognition is by analyzing the subject’s biosignals (e.g. electrodermal activity, blood-volume-pulse, peripheral temperature, electrocardiogram signals) during emotion elicitation (Canento et al., 2011) . In particular, the electroencephalogram (EEG) is a noninvasive, cost effective and simple technique, with good temporal resolution (Mak and Wolpaw, 2009), providing a measure of what is happening in the brain, the physiological source of emotions. It has long been noted that the EEG can provide information about the emotional state of the subject, in particular regarding frontal asymmetries (Ahern and Schwartz, 1985; Coan and Allen, 2004) . In this paper, we make an exploratory analysis of the EEG acquired from subjects performing a stressful task, demanding high concentration levels over a long period of time. This experiment mimics what may happen during an interactive educational game, where it would be useful to detect when the subject is growing tired of performing a certain task, seamlessly switching to another, less demanding activity.  \nTraditionally, EEG signals are analyzed by extracting band power features, given that brain activity, as measured from the scalp, exhibits an oscillatory behavior whose dynamics (a","cbCaiajDu7r0D6FA","https://ap.wps.com/l/cbCaiajDu7r0D6FA","pdf",2156725,"English","# 1 Introduction\n# 2 Emotion Elicitation and Data Acquisition","[{\"question\":\"What is the main goal of the paper?\",\"answer\":\"The paper aims to automatically assess a subject’s emotional state using exploratory EEG analysis combined with clustering methods.\"},{\"question\":\"How are the EEG features constructed for clustering?\",\"answer\":\"Features come from three processing routes: raw EEG, ICA-processed EEG to reduce eye artifacts, and EMD to extract data-driven modes, from which band-power features and phase-locking factors are computed.\"},{\"question\":\"What key clustering findings are reported?\",\"answer\":\"Clustering results show interesting patterns, including a tendency toward fewer clusters and clusters that often consist of consecutive test lines.\"}]","Exploratory EEG Analysis Using Clustering and Phase-Locking Factor | PDF",1791050779,25]