{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T04:46:03Z","timestamp":1778215563702,"version":"3.51.4"},"reference-count":94,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T00:00:00Z","timestamp":1589760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). A defining feature is rigid structures to fixate on pre-defined goals. Other SM strategies instead concentrate on continuous improvement by giving directions. An example of this group is the \u201cHOSHIN KANRI TREE\u201d (HKT). One way of analyzing the dissimilarities, the advantages and disadvantages of these groups, is to examine the neurological patterns of workers as they are applying these. This paper aims to achieve this evaluation through non-invasive electroencephalography (EEG) sensors, which capture the electrical activity of the brain. A deep learning (DL) soft sensor is used to classify the recorded data with an accuracy of 96.5%. Through this result and an analysis using the correlations of the EEG signals, it has been possible to detect relevant characteristics and differences in the brain\u2019s activity. In conclusion, these findings are expected to help assess SM systems and give guidance to Industry 4.0 leaders.<\/jats:p>","DOI":"10.3390\/s20102860","type":"journal-article","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T11:34:14Z","timestamp":1589801654000},"page":"2860","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8917-2041","authenticated-orcid":false,"given":"Daniel","family":"Schmidt","sequence":"first","affiliation":[{"name":"Department of Business Intelligence, Escuela T\u00e9cnica Superior de Ingenieros Industriales, Universidad Polit\u00e9cnica de Madrid, 28006 Madrid, Spain"},{"name":"Matthews International GmbH, Gutenbergstra\u00dfe 1-3, 48691 Vreden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2423-1474","authenticated-orcid":false,"given":"Javier","family":"Villalba Diez","sequence":"additional","affiliation":[{"name":"Department of Business Intelligence, Escuela T\u00e9cnica Superior de Ingenieros Industriales, Universidad Polit\u00e9cnica de Madrid, 28006 Madrid, Spain"},{"name":"Hochschule Heilbronn, Fakult\u00e4t Management und Vertrieb, Campus Schw\u00e4bisch Hall, 74523 Schw\u00e4bisch Hall, Germany"},{"name":"Department of Artificial Intelligence, Escuela T\u00e9cnica Superior de Ingenieros Inform\u00e1ticos, Universidad Polit\u00e9cnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9677-6764","authenticated-orcid":false,"given":"Joaqu\u00edn","family":"Ordieres-Mer\u00e9","sequence":"additional","affiliation":[{"name":"Department of Business Intelligence, Escuela T\u00e9cnica Superior de Ingenieros Industriales, Universidad Polit\u00e9cnica de Madrid, 28006 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman","family":"Gevers","sequence":"additional","affiliation":[{"name":"Matthews International GmbH, Gutenbergstra\u00dfe 1-3, 48691 Vreden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joerg","family":"Schwiep","sequence":"additional","affiliation":[{"name":"Matthews International GmbH, Gutenbergstra\u00dfe 1-3, 48691 Vreden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7145-1974","authenticated-orcid":false,"given":"Martin","family":"Molina","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Escuela T\u00e9cnica Superior de Ingenieros Inform\u00e1ticos, Universidad Polit\u00e9cnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s12599-014-0334-4","article-title":"Industry 4.0","volume":"6","author":"Lasi","year":"2014","journal-title":"Bus. 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