{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T07:51:31Z","timestamp":1773388291471,"version":"3.50.1"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"ANID Chile through the project ANILLO","award":["ACT210053"],"award-info":[{"award-number":["ACT210053"]}]},{"name":"ANID Chile through the project DOCTORADO NACIONAL","award":["21221195"],"award-info":[{"award-number":["21221195"]}]},{"name":"ANID Chile through the project FONDECYT","award":["3230682"],"award-info":[{"award-number":["3230682"]}]},{"name":"ANID Chile through the project FONDECYT","award":["1241965"],"award-info":[{"award-number":["1241965"]}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/X005267\/1"],"award-info":[{"award-number":["MR\/X005267\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3554137","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T18:42:07Z","timestamp":1742841727000},"page":"56053-56066","source":"Crossref","is-referenced-by-count":1,"title":["EEG Multi-Mode Oscillatory Brain State Allocation Using Switching Spectral Gaussian Processes"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0869-8215","authenticated-orcid":false,"given":"Yunier","family":"Prieur-Coloma","sequence":"first","affiliation":[{"name":"Escuela de Ingeniería Civil Biomédica, Universidad de Valparaíso, Valparaíso, Chile"}]},{"given":"Felipe A.","family":"Torres","sequence":"additional","affiliation":[{"name":"Departamento de Computación e Industrias, Universidad Católica del Maule, Talca, Chile"}]},{"given":"Nelson J.","family":"Trujillo-Barreto","sequence":"additional","affiliation":[{"name":"School of Psychology, Manchester Metropolitan University, Manchester, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9822-1092","authenticated-orcid":false,"given":"Wael","family":"El-Deredy","sequence":"additional","affiliation":[{"name":"Escuela de Ingeniería Civil Biomédica, Universidad de Valparaíso, Valparaíso, Chile"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2022.0214"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.70018"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.460020107"},{"issue":"6","key":"ref4","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.tics.2010.04.004","article-title":"Large-scale brain networks in cognition: Emerging methods and principles","volume":"14","author":"Bressler","year":"2010","journal-title":"Trends Cognit. Sci."},{"issue":"6","key":"ref5","first-page":"1287","article-title":"Early diagnosis of Alzheimer\u2019s disease: The role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts","volume-title":"Clin. Neurophysiol.","volume":"131","author":"Rossini","year":"2020"},{"issue":"10","key":"ref6","doi-asserted-by":"crossref","first-page":"4084","DOI":"10.1038\/s41380-023-02215-8","article-title":"The use of neuroimaging techniques in the early and differential diagnosis of dementia","volume":"28","author":"Chouliaras","year":"2023","journal-title":"Mol. Psychiatry"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.neuroimage.2017.11.062","article-title":"EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review","volume":"180","author":"Michel","year":"2018","journal-title":"NeuroImage"},{"issue":"9","key":"ref8","doi-asserted-by":"crossref","first-page":"e555","DOI":"10.1016\/S2589-7500(21)00101-1","article-title":"Discovery of Parkinson\u2019s disease states and disease progression modelling: A longitudinal data study using machine learning","volume":"3","author":"Severson","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3354711"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.7554\/elife.01867"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.neuroimage.2013.04.046","article-title":"A switching multi-scale dynamical network model of EEG\/MEG","volume":"83","author":"Olier","year":"2013","journal-title":"NeuroImage"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2014.12.010"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119595"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.neuroimage.2016.12.061","article-title":"The dynamic functional connectome: State-of-the-art and perspectives","volume":"160","author":"Preti","year":"2017","journal-title":"NeuroImage"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.neuroimage.2017.10.003","article-title":"Dynamics of large-scale electrophysiological networks: A technical review","volume":"180","author":"O\u2019Neill","year":"2018","journal-title":"NeuroImage"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.neuroimage.2019.06.006","article-title":"Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity","volume":"200","author":"Tewarie","year":"2019","journal-title":"NeuroImage"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1162\/netn_a_00182"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neuroimage.2015.04.030","article-title":"Dynamic recruitment of resting state sub-networks","volume":"115","author":"O\u2019Neill","year":"2015","journal-title":"NeuroImage"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3277731"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-05316-z"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.neuroimage.2015.11.047","article-title":"Spectrally resolved fast transient brain states in electrophysiological data","volume":"126","author":"Vidaurre","year":"2016","journal-title":"NeuroImage"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102366","article-title":"Multi-dynamic modelling reveals strongly time-varying resting fMRI correlations","volume":"77","author":"Pervaiz","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1126\/science.1099745"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/nrn3137"},{"issue":"10","key":"ref25","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.tics.2005.08.011","article-title":"A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence","volume":"9","author":"Fries","year":"2005","journal-title":"Trends Cognit. Sci."},{"key":"ref26","article-title":"Theta\u2013gamma coupling as a ubiquitous brain mechanism: Implications for memory, attention, dreaming, imagination, and consciousness","volume":"59","author":"Ursino","year":"2024","journal-title":"Current Opinion Behav. Sci."},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2018.00101"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1038\/s41386-020-0759-z"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.schres.2017.01.044","article-title":"Impaired theta-gamma coupling during working memory performance in schizophrenia","volume":"189","author":"Barr","year":"2017","journal-title":"Schizophrenia Res."},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1093\/braincomms\/fcae121"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1093\/scan\/nsaa056"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jneumeth.2015.10.010","article-title":"Extracting spatial\u2013temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition","volume":"258","author":"Brunton","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120358"},{"issue":"2","key":"ref34","doi-asserted-by":"crossref","first-page":"114","DOI":"10.3390\/dynamics2020006","article-title":"On the biophysical complexity of brain dynamics: An outlook","volume":"2","author":"Shettigar","year":"2022","journal-title":"Dynamics"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref36","first-page":"6684","article-title":"Spectral mixture kernels for multi-output Gaussian processes","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Parra"},{"key":"ref37","first-page":"1","article-title":"GP kernels for cross-spectrum analysis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Ulrich"},{"issue":"19","key":"ref38","doi-asserted-by":"crossref","first-page":"6765","DOI":"10.3390\/app10196765","article-title":"A discriminative multi-output Gaussian processes scheme for brain electrical activity analysis","volume":"10","author":"Torres-Valencia","year":"2020","journal-title":"Appl. Sci."},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3159653"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/c2014-0-02508-7"},{"key":"ref41","first-page":"1067","article-title":"Gaussian process kernels for pattern discovery and extrapolation","volume-title":"Proc. 30th Int. Conf. Mach. Learn.","volume":"28","author":"Wilson"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-1494-6"},{"key":"ref43","first-page":"2211","article-title":"Multiple kernel learning algorithms","volume":"12","author":"G\u00f6nen","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1561\/2200000036"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1198\/016214502753479464"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.11.047"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1038\/s42005-022-00950-y"},{"issue":"1","key":"ref48","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/nimg.2001.0978","article-title":"Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain","volume":"15","author":"Tzourio-Mazoyer","year":"2002","journal-title":"NeuroImage"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-80510-2"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3390\/data8060095"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.1074-21.2021"},{"key":"ref52","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2024.120804","article-title":"Unveiling the core functional networks of cognition: An ontology-guided machine learning approach","volume":"298","author":"Wu","year":"2024","journal-title":"NeuroImage"},{"issue":"23","key":"ref53","doi-asserted-by":"crossref","first-page":"2728","DOI":"10.3390\/diagnostics14232728","article-title":"Functional brain network disruptions in Parkinson\u2019s disease: Insights from information theory and machine learning","volume":"14","author":"Akg\u00fcller","year":"2024","journal-title":"Diagnostics"},{"key":"ref54","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2021.118850","article-title":"Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales","volume":"247","author":"Coquelet","year":"2022","journal-title":"NeuroImage"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neurobiolaging.2023.05.010","article-title":"Spectral power ratio as a measure of EEG changes in mild cognitive impairment due to Alzheimer\u2019s disease: A case-control study","volume":"130","author":"Flores-Sandoval","year":"2023","journal-title":"Neurobiol. Aging"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-33973-8"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2023.1142085"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10937758.pdf?arnumber=10937758","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T07:54:37Z","timestamp":1743839677000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10937758\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":57,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3554137","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}