{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:36:00Z","timestamp":1760236560135,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,12]],"date-time":"2021-12-12T00:00:00Z","timestamp":1639267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["This work was supported by the Consejo Nacional de Ciencia y Tecnolog\u00eda \u2014 CONACyT [Scholarship to C.C.O scholarship 480527, O.P. with CVU 713526 and J.B.L. with CVU 745514]"],"award-info":[{"award-number":["This work was supported by the Consejo Nacional de Ciencia y Tecnolog\u00eda \u2014 CONACyT [Scholarship to C.C.O scholarship 480527, O.P. with CVU 713526 and J.B.L. with CVU 745514]"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (\u03b4, \u03b8, \u03b1 and \u03b2) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu\u2019s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.<\/jats:p>","DOI":"10.3390\/s21248305","type":"journal-article","created":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T01:29:33Z","timestamp":1639358973000},"page":"8305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8671-6549","authenticated-orcid":false,"given":"C\u00e9sar","family":"Covantes-Osuna","sequence":"first","affiliation":[{"name":"Departamento de Bioingenier\u00eda Traslacional, CUCEI, Universidad de Guadalajara, Guadalajara 44430, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3733-9863","authenticated-orcid":false,"given":"Jhonatan B.","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"Departamento de Bioingenier\u00eda Traslacional, CUCEI, Universidad de Guadalajara, Guadalajara 44430, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5382-6127","authenticated-orcid":false,"given":"Omar","family":"Paredes","sequence":"additional","affiliation":[{"name":"Departamento de Bioingenier\u00eda Traslacional, CUCEI, Universidad de Guadalajara, Guadalajara 44430, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1319-8493","authenticated-orcid":false,"given":"Hugo","family":"V\u00e9lez-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Departamento de Bioingenier\u00eda Traslacional, CUCEI, Universidad de Guadalajara, Guadalajara 44430, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6459-8070","authenticated-orcid":false,"given":"Rebeca","family":"Romo-V\u00e1zquez","sequence":"additional","affiliation":[{"name":"Departamento de Bioingenier\u00eda Traslacional, CUCEI, Universidad de Guadalajara, Guadalajara 44430, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6158","DOI":"10.1126\/science.1238411","article-title":"Structural and functional brain networks: From connections to cognition","volume":"342","author":"Park","year":"2013","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cmpb.2004.10.009","article-title":"Classification of EEG signals using neural network and logistic regression","volume":"78","author":"Subasi","year":"2005","journal-title":"Comput. 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