[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-137156-105":59,"doc-detail-137156-en":131},{"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":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","complexity-measures-for-eeg-microstate-sequences-concepts-and-algorithms","Complexity measures for EEG microstate sequences-concepts and algorithms","","EEG microstate sequence analysis quantifies properties of ongoing brain electrical activity with complex dynamics across time scales. This report reviews recent advances for microstate sequence complexity, organizing methods by distinct complexity concepts and assessing excess entropy as an underused quantity in this research area. Entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents are evaluated on Potts model data and then applied to wakefulness and nonREM EEG microstate sequences using Markov surrogate data.",{"@graph":69,"@context":123},[70,84,106],{"@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/complexity-measures-for-eeg-microstate-sequences-concepts-and-algorithms/137156/",{"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/complexity-measures-for-eeg-microstate-sequences-concepts-and-algorithms/137156.png","ImageObject",300,407,{"name":92,"@type":93},"Patrick","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-08-22",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does EEG microstate sequence complexity analysis aim to quantify?","Question",{"text":113,"@type":114},"It quantifies properties of ongoing brain electrical activity, reflecting complex dynamics across multiple time scales in EEG microstate sequences.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which complexity metrics are evaluated and compared in the study?",{"text":118,"@type":114},"The study determines entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents, using both Potts model data and EEG microstate sequences.",{"name":120,"@type":111,"acceptedAnswer":121},"How do entropy rate, LZC, excess entropy, and Hurst exponents differ in what they describe?",{"text":122,"@type":114},"Entropy rate and LZC characterize Kolmogorov complexity (randomness), while excess entropy and Hurst exponents characterize statistical complexity that peaks at intermediate randomness levels.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},137156,1787400757,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","􀂦   \nUNSW Sydney  \nKlinikum Lüneburg  \nKiel University  \nKiel University  \nUniversity of Buenos Aires  \nKiel University  \nMay 10th, 2023  \n [https://doi.org/10.21203/rs.3.rs-2878411/v1](https://doi.org/10.21203/rs.3.rs-2878411/v1)  \n 􀁱 􀅏 This work is licensed under a Creative Commons Attribution 4.0 International License.  \nRead Full License  \n No competing interests reported.  \n A version of this preprint was published at Brain Topography on September 26th, 2023. See the published version at [https://doi.org/10.1007/s10548-023-01006-2](https://doi.org/10.1007/s10548-023-01006-2) .  \nComplexity measures for EEG microstate sequences-concepts and algorithms  \nFrederic von Wegner 1*, Milena Wiemers2 , Gesine Hermann3 , Inken T¨odt3 , Enzo Tagliazucchi4 , Helmut Laufs3  \n1* School of Biomedical Sciences, University of New South Wales (UNSW), Wallace Wurth, Kensington, 2052, NSW, Australia.  \n2 Department of Neurology and Clinical Neurophysiology, L¨uneburg Hospital, B¨ogelstrasse 1, L¨uneburg, 21339, Germany.  \n3 Dept. of Neurology, Christian-Albrechts University, Arnold-Heller-Strasse 3, Kiel, 24105, Germany.  \n4 Department of Physics, University of Buenos Aires, Buenos Aires, 1428, Argentina.  \n*Corresponding author(s). E-mail(s): [f.vonwegner@unsw.edu.au](f.vonwegner@unsw.edu.au) ;  \nAbstract  \nEEG microstate sequence analysis quanti􀀌es properties of ongoing brain electrical activity which is known to exhibit complex dynamics across many time scales. In this report we review recent developments in quantifying microstate sequence complexity, we classify these approaches with regard to di􀀋erent complexity concepts, and we evaluate excess entropy as a yet unexplored quantity in microstate research.  \nWe determined the quantities entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents on Potts model data, a discrete statistical mechanics model with a temperature-controlled phase transition. We then applied the same techniques to EEG microstate sequences from wakefulness and nonREM sleep stages and used 􀀌rst-order Markov surrogate data to determine which time scales contributed to the di􀀋erent complexity measures.  \nWe demonstrate that entropy rate and LZC measure the Kolmogorov complexity (randomness) of microstate sequences, whereas excess entropy and Hurst exponents describe statistical complexity which attains its maximum at intermediate levels of randomness. We con􀀌rmed the equivalence of entropy rate and LZC when the LZ-76 algorithm is used, a result previously reported for neural spike train analysis (Amig􀀓o et al, 2004) . Surrogate data analyses prove that entropybased quantities and LZC focus on short-range temporal correlations, whereas  \n1  \n001  \n002  \n003  \n004  \n005  \n006  \n007  \n008  \n009  \n010  \n011  \n012  \n013  \n014  \n015  \n016  \n017  \n018  \n019  \n020  \n021  \n022  \n023  \n024  \n025  \n026  \n027  \n028  \n029  \n030  \n031  \n032  \n033  \n034  \n035  \n036  \n037  \n038  \n039  \n040  \n041  \n042  \n043  \n044  \n045  \n046  \n047  \n048  \n049  \n050  \n051  \n052  \n053  \n054  \n055  \n056  \n057  \n058  \n059  \n060  \n061  \n062  \n063  \n064  \n065  \n066  \n067  \n068  \n069  \n070  \n071  \n072  \n073  \n074  \n075  \n076  \n077  \n078  \n079  \n080  \n081  \n082  \n083  \n084  \n085  \n086  \n087  \n088  \n089  \n090  \n091  \n092  \nHurst exponents include short and long time scales. Sleep data analysis reveals that deeper sleep stages are accompanied by a decrease in Kolmogorov complexity and an increase in statistical complexity.  \nRegarding the practical use of these methods, we suggest that LZC can be used as an e􀀎cient entropy rate estimator that avoids the estimation of joint entropies, whereas entropy rate estimation via joint entropies has the advantage of providing excess entropy as the second parameter of the same linear 􀀌t.  \nWe conclude that metrics of statistical complexity are a useful addition to microstate analysis and address a complexity concept that is not yet covered by existing microstate algorithms while being actively explored","cbCaibVczMamh4cA","https://ap.wps.com/l/cbCaibVczMamh4cA","pdf",2677196,29,"English","# Abstract\n# Introduction\n## Complexity concepts and measurement\n# Concepts of complexity","[{\"question\":\"What does EEG microstate sequence complexity analysis aim to quantify?\",\"answer\":\"It quantifies properties of ongoing brain electrical activity, reflecting complex dynamics across multiple time scales in EEG microstate sequences.\"},{\"question\":\"Which complexity metrics are evaluated and compared in the study?\",\"answer\":\"The study determines entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents, using both Potts model data and EEG microstate sequences.\"},{\"question\":\"How do entropy rate, LZC, excess entropy, and Hurst exponents differ in what they describe?\",\"answer\":\"Entropy rate and LZC characterize Kolmogorov complexity (randomness), while excess entropy and Hurst exponents characterize statistical complexity that peaks at intermediate randomness levels.\"}]","Complexity measures for EEG microstate sequences-concepts and algorithms | PDF",73]