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In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.<\/jats:p>","DOI":"10.3390\/s24041174","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T03:50:27Z","timestamp":1707709827000},"page":"1174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1563-9934","authenticated-orcid":false,"given":"Aura","family":"Hern\u00e1ndez-Sabat\u00e9","sequence":"first","affiliation":[{"name":"Computer Vision Center (CVC), C\/ Sitges, Edifici O, 08193 Bellaterra, Spain"},{"name":"Engineering School, Universitat Aut\u00f2noma de Barcelona, C\/ Sitges, Edifici Q, 08193 Bellaterra, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6287-7797","authenticated-orcid":false,"given":"Jos\u00e9","family":"Yauri","sequence":"additional","affiliation":[{"name":"Computer Vision Center (CVC), C\/ Sitges, Edifici O, 08193 Bellaterra, Spain"}]},{"given":"Pau","family":"Folch","sequence":"additional","affiliation":[{"name":"Engineering School, Universitat Aut\u00f2noma de Barcelona, C\/ Sitges, Edifici Q, 08193 Bellaterra, Spain"}]},{"given":"Daniel","family":"\u00c1lvarez","sequence":"additional","affiliation":[{"name":"Aslogic, Av. Electricitat, 1-21, 08191 Rub\u00ed, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2770-4767","authenticated-orcid":false,"given":"Debora","family":"Gil","sequence":"additional","affiliation":[{"name":"Computer Vision Center (CVC), C\/ Sitges, Edifici O, 08193 Bellaterra, Spain"},{"name":"Engineering School, Universitat Aut\u00f2noma de Barcelona, C\/ Sitges, Edifici Q, 08193 Bellaterra, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neubiorev.2012.10.003","article-title":"Measuring Neurophysiological Signals in Aircraft Pilots and Car Drivers for the Assessment of Mental Workload, Fatigue and Drowsiness","volume":"44","author":"Borghini","year":"2014","journal-title":"Neurosci. Biobehav. 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