{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T19:08:04Z","timestamp":1785352084850,"version":"3.55.0"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,24]],"date-time":"2022-12-24T00:00:00Z","timestamp":1671840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish State Research Agency (AEI) of the Ministry of Science and Innovation (MCIN)","award":["PID2020-112586RBI00\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["PID2020-112586RBI00\/AEI\/10.13039\/501100011033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be accurately developed. In this work, we focus on biometric authentication, as it depends on the user\u2019s inherent characteristics and, therefore, offers personalized authentication systems. Specifically, we propose an electrocardiogram (EEG)-based user authentication system by employing One-Class and Multi-Class Machine Learning classifiers. In this sense, the main novelty of this article is the introduction of Isolation Forest and Local Outlier Factor classifiers as new tools for user authentication and the investigation of their suitability with EEG data. Additionally, we identify the EEG channels and brainwaves with greater contribution to the authentication and compare them with the traditional dimensionality reduction techniques, Principal Component Analysis, and \u03c72 statistical test. In our final proposal, we elaborate on a hybrid system resistant to random forgery attacks using an Isolation Forest and a Random Forest classifiers, obtaining a final accuracy of 82.3%, a precision of 91.1% and a recall of 75.3%.<\/jats:p>","DOI":"10.3390\/s23010186","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:55:07Z","timestamp":1672109707000},"page":"186","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2637-8901","authenticated-orcid":false,"given":"Luis","family":"Hern\u00e1ndez-\u00c1lvarez","sequence":"first","affiliation":[{"name":"Computer Security Lab, Universidad Carlos III de Madrid, 28911 Legan\u00e9s, Spain"},{"name":"Institute of Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-3792","authenticated-orcid":false,"given":"Elena","family":"Barbierato","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Food, Environment and Forestry, University of Florence, 50144 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4516-8910","authenticated-orcid":false,"given":"Stefano","family":"Caputo","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6389-0221","authenticated-orcid":false,"given":"Lorenzo","family":"Mucchi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6980-2683","authenticated-orcid":false,"given":"Luis","family":"Hern\u00e1ndez Encinas","sequence":"additional","affiliation":[{"name":"Institute of Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. 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