{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:40:57Z","timestamp":1769856057178,"version":"3.49.0"},"reference-count":64,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Integrated Infrastructure Operational Program for the project: Creation of a Digital Biobank to support the systemic public research infrastructure","award":["313011AFG4"],"award-info":[{"award-number":["313011AFG4"]}]},{"name":"European Regional Development Fund","award":["313011AFG4"],"award-info":[{"award-number":["313011AFG4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the development of human society, there is an increasing importance for reliable person identification and authentication to protect a person\u2019s material and intellectual property. Person identification based on brain signals has captured substantial attention in recent years. These signals are characterized by original patterns for a specific person and are capable of providing security and privacy of an individual in biometric identification. This study presents a biometric identification method based on a novel paradigm with accrual cognitive brain load from relaxing with eyes closed to the end of a serious game, which includes three levels with increasing difficulty. The used database contains EEG data from 21 different subjects. Specific patterns of EEG signals are recognized in the time domain and classified using a 1D Convolutional Neural Network proposed in the MATLAB environment. The ability of person identification based on individual tasks corresponding to a given degree of load and their fusion are examined by 5-fold cross-validation. Final accuracies of more than 99% and 98% were achieved for individual tasks and task fusion, respectively. The reduction of EEG channels is also investigated. The results imply that this approach is suitable to real applications.<\/jats:p>","DOI":"10.3390\/s22197154","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T23:07:55Z","timestamp":1663888075000},"page":"7154","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["EEG-Based Person Identification during Escalating Cognitive Load"],"prefix":"10.3390","volume":"22","author":[{"given":"Ivana","family":"Kralikova","sequence":"first","affiliation":[{"name":"Department of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9875-8551","authenticated-orcid":false,"given":"Branko","family":"Babusiak","sequence":"additional","affiliation":[{"name":"Department of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8216-3214","authenticated-orcid":false,"given":"Maros","family":"Smondrk","sequence":"additional","affiliation":[{"name":"Department of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"ref_1","first-page":"39","article-title":"Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning","volume":"8","author":"Kouamo","year":"2016","journal-title":"J. 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