{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:33:56Z","timestamp":1780619636894,"version":"3.54.1"},"reference-count":71,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006754","name":"US Army Combat Capabilities Development Command Army Research Laboratory","doi-asserted-by":"publisher","award":["W911NF-24-2-0013"],"award-info":[{"award-number":["W911NF-24-2-0013"]}],"id":[{"id":"10.13039\/100006754","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neuroscience Methods"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.jneumeth.2026.110744","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T17:02:59Z","timestamp":1773680579000},"page":"110744","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["MapperEEG: A topological approach to brain state clustering in EEG recordings"],"prefix":"10.1016","volume":"431","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8026-5492","authenticated-orcid":false,"given":"Brittany","family":"Story","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0953-831X","authenticated-orcid":false,"given":"Zhibin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2090-8257","authenticated-orcid":false,"given":"Ramesh","family":"Srinivasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Kerick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Boothe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5166-4224","authenticated-orcid":false,"given":"Piotr J.","family":"Franaszczuk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jneumeth.2026.110744_b1","article-title":"Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity","volume":"17","author":"Almohammadi","year":"2023","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.jneumeth.2026.110744_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102196","article-title":"Parameter investigation of topological data analysis for EEG signals","volume":"63","author":"Alt\u0131ndi\u015f","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"issue":"3","key":"10.1016\/j.jneumeth.2026.110744_b3","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.cegh.2020.01.008","article-title":"A multi stage EEG data classification using k-means and feed forward neural network","volume":"8","author":"Bablani","year":"2020","journal-title":"Clin. Epidemiology Glob. Health"},{"key":"10.1016\/j.jneumeth.2026.110744_b4","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2025.1541062","article-title":"Enhanced visibility graph for EEG classification","volume":"19","author":"Belhadi","year":"2025","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.jneumeth.2026.110744_b5","doi-asserted-by":"crossref","first-page":"16","DOI":"10.3389\/fninf.2015.00016","article-title":"The PREP pipeline: standardized preprocessing for large-scale EEG analysis","volume":"9","author":"Bigdely-Shamlo","year":"2015","journal-title":"Front. Neuroinformatics"},{"key":"10.1016\/j.jneumeth.2026.110744_b6","series-title":"2018 International Conference on Intelligent Systems","first-page":"795","article-title":"A study on mental state classification using EEG-based brain-machine interface","author":"Bird","year":"2018"},{"key":"10.1016\/j.jneumeth.2026.110744_b7","series-title":"Rhythms of the Brain","author":"Buzsaki","year":"2006"},{"key":"10.1016\/j.jneumeth.2026.110744_b8","doi-asserted-by":"crossref","first-page":"93","DOI":"10.3389\/fneur.2014.00093","article-title":"Network connectivity in epilepsy: resting state fMRI and EEG-fMRI contributions","volume":"5","author":"Centeno","year":"2014","journal-title":"Front. Neurol."},{"issue":"2","key":"10.1016\/j.jneumeth.2026.110744_b9","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.conb.2010.02.015","article-title":"Beta-band oscillations\u2013signalling the status quo?","volume":"20","author":"Engel","year":"2010","journal-title":"Current Opin. Neurobiology"},{"key":"10.1016\/j.jneumeth.2026.110744_b10","series-title":"Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","author":"Ester","year":"1996"},{"key":"10.1016\/j.jneumeth.2026.110744_b11","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.neuroimage.2017.03.064","article-title":"Can brain state be manipulated to emphasize individual differences in functional connectivity?","volume":"160","author":"Finn","year":"2017","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2026.110744_b12","series-title":"2022 IEEE 3rd International Conference on Human-Machine Systems","first-page":"1","article-title":"A neurological approach to classify trust through EEG signals using machine learning techniques","author":"Firoz","year":"2022"},{"issue":"3","key":"10.1016\/j.jneumeth.2026.110744_b13","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","article-title":"Community detection in graphs","volume":"486","author":"Fortunato","year":"2010","journal-title":"Phys. Rep."},{"issue":"1","key":"10.1016\/j.jneumeth.2026.110744_b14","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.neuron.2015.09.034","article-title":"Rhythms for cognition: communication through coherence","volume":"88","author":"Fries","year":"2015","journal-title":"Neuron"},{"issue":"5","key":"10.1016\/j.jneumeth.2026.110744_b15","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1109\/JPROC.2017.2786710","article-title":"Applications of community detection techniques to brain graphs: algorithmic considerations and implications for neural function","volume":"106","author":"Garcia","year":"2018","journal-title":"Proc. IEEE"},{"issue":"3","key":"10.1016\/j.jneumeth.2026.110744_b16","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1162\/netn_a_00093","article-title":"Generating dynamical neuroimaging spatiotemporal representations (dyneuSR) using topological data analysis","volume":"3","author":"Geniesse","year":"2019","journal-title":"Netw. Neurosci."},{"issue":"2","key":"10.1016\/j.jneumeth.2026.110744_b17","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.nec.2010.11.001","article-title":"Overview of functional magnetic resonance imaging","volume":"22","author":"Glover","year":"2011","journal-title":"Neurosurg. Clin. North Am."},{"key":"10.1016\/j.jneumeth.2026.110744_b18","first-page":"1","article-title":"Deconstructing the mapper algorithm to extract richer topological and temporal features from functional neuroimaging data","author":"Hasegan","year":"2024","journal-title":"Netw. Neurosci."},{"key":"10.1016\/j.jneumeth.2026.110744_b19","series-title":"Algebraic Topology","author":"Hatcher","year":"2000"},{"key":"10.1016\/j.jneumeth.2026.110744_b20","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1146\/annurev-bioeng-062117-120853","article-title":"Electrophysiological source imaging: a noninvasive window to brain dynamics","volume":"20","author":"He","year":"2018","journal-title":"Annu. Rev. Biomed. Eng."},{"issue":"5","key":"10.1016\/j.jneumeth.2026.110744_b21","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MEMB.2006.1705745","article-title":"Sensors for brain-computer interfaces","volume":"25","author":"Hochberg","year":"2006","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"10.1016\/j.jneumeth.2026.110744_b22","series-title":"EEG Signal Processing and Feature Extraction","year":"2019"},{"key":"10.1016\/j.jneumeth.2026.110744_b23","series-title":"2019 International Joint Conference on Neural Networks","first-page":"1","article-title":"A time-frequency based machine learning system for brain states classification via EEG signal processing","author":"Ieracitano","year":"2019"},{"issue":"120371","key":"10.1016\/j.jneumeth.2026.110744_b24","article-title":"Towards a dynamical understanding of microstate analysis of M\/EEG data","volume":"281","author":"Jajcay","year":"2023","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2026.110744_b25","series-title":"Principal Component Analysis","author":"Jolliffe","year":"2002"},{"issue":"2","key":"10.1016\/j.jneumeth.2026.110744_b26","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1111\/1469-8986.3720163","article-title":"Removing electroencephalographic artifacts by blind source separation","volume":"37","author":"Jung","year":"2000","journal-title":"Psychophysiology"},{"key":"10.1016\/j.jneumeth.2026.110744_b27","series-title":"Handbook of EEG Interpretation","author":"Kaplan","year":"2007"},{"issue":"3","key":"10.1016\/j.jneumeth.2026.110744_b28","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0283418","article-title":"Neural and behavioral adaptations to frontal theta neurofeedback training: a proof of concept study","volume":"18","author":"Kerick","year":"2023","journal-title":"PLOS ONE"},{"issue":"10","key":"10.1016\/j.jneumeth.2026.110744_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.celrep.2020.108128","article-title":"Brain states and transitions: Insights from computational neuroscience","volume":"32","author":"Kringelbach","year":"2020","journal-title":"Cell Rep."},{"issue":"5","key":"10.1016\/j.jneumeth.2026.110744_b30","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.jneumeth.2026.110744_b31","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3389\/fnhum.2018.00014","article-title":"EEG-based brain\u2013computer interfaces for communication and rehabilitation of people with motor impairment: A novel approach of the 21st century","volume":"12","author":"Lazarou","year":"2018","journal-title":"Front. Hum. Neurosci."},{"issue":"7","key":"10.1016\/j.jneumeth.2026.110744_b32","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0040370","article-title":"Clustering of resting state networks","volume":"7","author":"Lee","year":"2012","journal-title":"PLOS One"},{"issue":"1","key":"10.1016\/j.jneumeth.2026.110744_b33","doi-asserted-by":"crossref","DOI":"10.1063\/5.0234902","article-title":"Symbolic dynamics of joint brain states during dyadic coordination","volume":"35","author":"Lima Dias Pinto","year":"2025","journal-title":"Chaos"},{"issue":"19","key":"10.1016\/j.jneumeth.2026.110744_b34","doi-asserted-by":"crossref","first-page":"3091","DOI":"10.1093\/bioinformatics\/btab553","article-title":"The topology of data: opportunities for cancer research","volume":"37","author":"Loughrey","year":"2021","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jneumeth.2026.110744_b35","doi-asserted-by":"crossref","DOI":"10.1098\/rsos.230022","article-title":"Seizure detection with reduced electroencephalogram channels: research trends and outlook","volume":"10","author":"Maher","year":"2023","journal-title":"R. Soc. Open Sci."},{"issue":"2","key":"10.1016\/j.jneumeth.2026.110744_b36","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1002\/hbm.1042","article-title":"Event-related changes in neuromagnetic activity associated with syncopation and synchronization timing tasks","volume":"14","author":"Mayville","year":"2001","journal-title":"Human Brain Mapping"},{"issue":"4","key":"10.1016\/j.jneumeth.2026.110744_b37","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1002\/hbm.10065","article-title":"Cortical and subcortical networks underlying syncopated and synchronized coordination revealed using fMRI","volume":"17","author":"Mayville","year":"2002","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.jneumeth.2026.110744_b38","series-title":"UMAP: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2020"},{"key":"10.1016\/j.jneumeth.2026.110744_b39","series-title":"2021 IEEE International Conference on Quantum Computing and Engineering","first-page":"471","article-title":"Experiments on fraud detection use case with QML and TDA mapper","author":"Mitra","year":"2021"},{"issue":"3","key":"10.1016\/j.jneumeth.2026.110744_b40","doi-asserted-by":"crossref","first-page":"180","DOI":"10.7555\/JBR.33.20190009","article-title":"Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD","volume":"34","author":"Moctezuma","year":"2020","journal-title":"J. Biomed. Research"},{"key":"10.1016\/j.jneumeth.2026.110744_b41","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0246769","article-title":"Effect of number and placement of EEG electrodes on measurement of neural tracking of speech","volume":"16","author":"Montoya-Mart\u00ednez","year":"2021","journal-title":"PLOS ONE"},{"key":"10.1016\/j.jneumeth.2026.110744_b42","series-title":"2019 18th IEEE International Conference on Machine Learning and Applications","first-page":"646","article-title":"Bayesian topological learning for brain state classification","author":"Nasrin","year":"2019"},{"key":"10.1016\/j.jneumeth.2026.110744_b43","series-title":"StatPearls","article-title":"EEG normal waveforms","author":"Nayak","year":"2025"},{"key":"10.1016\/j.jneumeth.2026.110744_b44","article-title":"Finding and evaluating community structure in networks","volume":"69","author":"Newman","year":"2004","journal-title":"Phys. Rev. E"},{"issue":"3","key":"10.1016\/j.jneumeth.2026.110744_b45","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1097\/WNP.0000000000000736","article-title":"A primer on EEG spectrograms","volume":"39","author":"Ng","year":"2022","journal-title":"J. Clin. Neurophysiol."},{"issue":"1","key":"10.1016\/j.jneumeth.2026.110744_b46","doi-asserted-by":"crossref","first-page":"8581","DOI":"10.1038\/ncomms9581","article-title":"Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury","volume":"6","author":"Nielson","year":"2015","journal-title":"Nature Communications"},{"key":"10.1016\/j.jneumeth.2026.110744_b47","series-title":"Affective physiological state analysis and interpretable predictive modeling of marksmanship in Go\/NoGo VR shooting difficulty task","author":"Pratiher","year":"2022"},{"issue":"1","key":"10.1016\/j.jneumeth.2026.110744_b48","doi-asserted-by":"crossref","first-page":"15154","DOI":"10.1038\/s41598-024-66110-0","article-title":"Power spectral density-based resting-state EEG classification of first-episode psychosis","volume":"14","author":"Redwan","year":"2024","journal-title":"Sci. Rep."},{"issue":"11","key":"10.1016\/j.jneumeth.2026.110744_b49","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.3390\/brainsci11111525","article-title":"Neural decoding of EEG signals with machine learning: A systematic review","volume":"11","author":"Saeidi","year":"2021","journal-title":"Brain Sci."},{"key":"10.1016\/j.jneumeth.2026.110744_b50","doi-asserted-by":"crossref","first-page":"4791","DOI":"10.1038\/s41467-022-32381-2","article-title":"Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest","volume":"13","author":"Saggar","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.jneumeth.2026.110744_b51","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1038\/s41467-018-03664-4","article-title":"Towards a new approach to reveal dynamical organization of the brain using topological data analysis","volume":"9","author":"Saggar","year":"2018","journal-title":"Nat. Commun."},{"key":"10.1016\/j.jneumeth.2026.110744_b52","series-title":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning","first-page":"298","article-title":"Depth of anesthesia monitoring method based on EEG microstate analysis and hidden markov model","author":"Si","year":"2021"},{"key":"10.1016\/j.jneumeth.2026.110744_b53","doi-asserted-by":"crossref","first-page":"2041","DOI":"10.1007\/s00521-017-3126-1","article-title":"Fractal dimension methods to determine optimum EEG electrode placement for concentration estimation","volume":"31","author":"Siamaknejad","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.jneumeth.2026.110744_b54","series-title":"Eurographics Symposium on Point-Based Graphics","article-title":"Topological methods for the analysis of high dimensional data sets and 3D object recognition","author":"Singh","year":"2007"},{"issue":"3","key":"10.1016\/j.jneumeth.2026.110744_b55","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/TAFFC.2020.2995769","article-title":"Multilevel longitudinal analysis of shooting performance as a function of stress and cardiovascular responses","volume":"12","author":"Spangler","year":"2021","journal-title":"IEEE Trans. Affect. Computing"},{"key":"10.1016\/j.jneumeth.2026.110744_b56","series-title":"Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants","author":"St. Louis","year":"2016"},{"key":"10.1016\/j.jneumeth.2026.110744_b57","series-title":"Topology- and Graph-Informed Imaging Informatics","first-page":"88","article-title":"Topological data analysis of resting-state fMRI suggests altered brain network topology in functional dyspepsia: a mapper-based parcellation approach","author":"Tassi","year":"2025"},{"key":"10.1016\/j.jneumeth.2026.110744_b58","first-page":"288","article-title":"Putting mapper on a map: cartographic visualizations of topological data analysis","volume":"3","author":"Thatcher","year":"2021","journal-title":"Abstr. ICA"},{"key":"10.1016\/j.jneumeth.2026.110744_b59","first-page":"2579","article-title":"Visualizing high-dimensional data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.jneumeth.2026.110744_b60","series-title":"Topology- and Graph-Informed Imaging Informatics","first-page":"76","article-title":"Exploitation of mapper algorithm in neuroimaging applications: A novel framework for outcomes prediction","author":"Vannoni","year":"2025"},{"issue":"42","key":"10.1016\/j.jneumeth.2026.110744_b61","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.21105\/joss.01315","article-title":"Kepler mapper: A flexible python implementation of the mapper algorithm.","volume":"4","author":"van Veen","year":"2019","journal-title":"J. Open Source Softw."},{"key":"10.1016\/j.jneumeth.2026.110744_b62","series-title":"Kepler Mapper: A flexible Python implementation of the Mapper algorithm","author":"van Veen","year":"2020"},{"key":"10.1016\/j.jneumeth.2026.110744_b63","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.asoc.2015.03.028","article-title":"EEG classification for the detection of mental states","volume":"32","author":"V\u00e9zard","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.jneumeth.2026.110744_b64","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2013.06.046","article-title":"Emotional state classification from EEG data using machine learning approach","volume":"129","author":"Wang","year":"2014","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jneumeth.2026.110744_b65","doi-asserted-by":"crossref","first-page":"308","DOI":"10.3389\/fnins.2015.00308","article-title":"EEG resolutions in detecting and decoding finger movements from spectral analysis","volume":"9","author":"Xiao","year":"2015","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.jneumeth.2026.110744_b66","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2021.761703","article-title":"Topological data analysis as a new tool for EEG processing","volume":"15","author":"Xu","year":"2021","journal-title":"Front. Neurosci."},{"issue":"1","key":"10.1016\/j.jneumeth.2026.110744_b67","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TFUZZ.2019.2903753","article-title":"Supervised network-based fuzzy learning of EEG signals for alzheimer\u2019s disease identification","volume":"28","author":"Yu","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"10","key":"10.1016\/j.jneumeth.2026.110744_b68","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1109\/TNSRE.2019.2939655","article-title":"Modulation effect of acupuncture on functional brain networks and classification of its manipulation with EEG signals","volume":"27","author":"Yu","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.jneumeth.2026.110744_b69","doi-asserted-by":"crossref","first-page":"2450","DOI":"10.1109\/TNSRE.2024.3421648","article-title":"Evaluation of acupuncture efficacy in modulating brain activity with periodic-aperiodic EEG measurements","volume":"32","author":"Yu","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"5","key":"10.1016\/j.jneumeth.2026.110744_b70","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1109\/TNSRE.2018.2828143","article-title":"Modulation of spectral power and functional connectivity in human brain by acupuncture stimulation","volume":"26","author":"Yu","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.jneumeth.2026.110744_b71","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Chalapathi, N., Rathore, A., Zhao, Y., Wang, B., 2021. Mapper Interactive: A scalable, extendable, and interactive toolbox for the visual exploration of high-dimensional data. In: 2021 IEEE 14th Pacific Visualization Symposium (PacificVis). Los Alamitos, CA, USA, pp. 101\u2013110. http:\/\/dx.doi.org\/10.1109\/PacificVis52677.2021.00021.","DOI":"10.1109\/PacificVis52677.2021.00021"}],"container-title":["Journal of Neuroscience Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165027026000749?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165027026000749?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T05:37:50Z","timestamp":1776145070000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0165027026000749"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":71,"alternative-id":["S0165027026000749"],"URL":"https:\/\/doi.org\/10.1016\/j.jneumeth.2026.110744","relation":{},"ISSN":["0165-0270"],"issn-type":[{"value":"0165-0270","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MapperEEG: A topological approach to brain state clustering in EEG recordings","name":"articletitle","label":"Article Title"},{"value":"Journal of Neuroscience Methods","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jneumeth.2026.110744","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"110744"}}