{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T00:16:17Z","timestamp":1793924177501,"version":"4.8.0"},"reference-count":62,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CODA: COntroller adaptive Digital Assistant","award":["101114765"],"award-info":[{"award-number":["101114765"]}]},{"name":"CODA: COntroller adaptive Digital Assistant","award":["101114838"],"award-info":[{"award-number":["101114838"]}]},{"name":"CODA: COntroller adaptive Digital Assistant","award":["2021-1-HR01-KA220-HED-000027562"],"award-info":[{"award-number":["2021-1-HR01-KA220-HED-000027562"]}]},{"name":"CODA: COntroller adaptive Digital Assistant","award":["2022-1-IT02-KA220-HED-000090206"],"award-info":[{"award-number":["2022-1-IT02-KA220-HED-000090206"]}]},{"name":"TRUSTY: TRUStworthy inTellingent sYstem for remote digital tower","award":["101114765"],"award-info":[{"award-number":["101114765"]}]},{"name":"TRUSTY: TRUStworthy inTellingent sYstem for remote digital tower","award":["101114838"],"award-info":[{"award-number":["101114838"]}]},{"name":"TRUSTY: TRUStworthy inTellingent sYstem for remote digital tower","award":["2021-1-HR01-KA220-HED-000027562"],"award-info":[{"award-number":["2021-1-HR01-KA220-HED-000027562"]}]},{"name":"TRUSTY: TRUStworthy inTellingent sYstem for remote digital tower","award":["2022-1-IT02-KA220-HED-000090206"],"award-info":[{"award-number":["2022-1-IT02-KA220-HED-000090206"]}]},{"name":"ERASMUS+ Programme of the EU","award":["101114765"],"award-info":[{"award-number":["101114765"]}]},{"name":"ERASMUS+ Programme of the EU","award":["101114838"],"award-info":[{"award-number":["101114838"]}]},{"name":"ERASMUS+ Programme of the EU","award":["2021-1-HR01-KA220-HED-000027562"],"award-info":[{"award-number":["2021-1-HR01-KA220-HED-000027562"]}]},{"name":"ERASMUS+ Programme of the EU","award":["2022-1-IT02-KA220-HED-000090206"],"award-info":[{"award-number":["2022-1-IT02-KA220-HED-000090206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Background: Understanding the impact of different learning materials in terms of comprehension and engagement is essential for optimizing educational strategies. While digital learning tools are increasingly used, offering and multiplying different educational solutions, their effects on learners\u2019 mental workload, attention, and engagement remain underexplored. This study aims to investigate how different types of learning content\u2014educational videos, academic videos, and text reading\u2014affect cognitive processing and engagement. Methods: Neurophysiological signals, including electroencephalography (EEG), electrodermal activity (EDA), and photoplethysmography (PPG), were recorded from experimental participants while they were engaged with each learning content. Subjective assessments of cognitive effort and engagement, together with a quiz to assess the knowledge acquisition, were collected through questionnaires for each tested content. Key neurophysiological metrics, such as engagement and Human Distraction Index (HDI), were computed and compared across conditions. Results: Our findings indicate that video-based learning materials, particularly educational videos with visual enhancements, elicited higher engagement and lower cognitive load compared to text-based learning. The text reading condition was associated with increased mental workload and a higher distraction index, suggesting greater cognitive demands. Correlation analyses confirmed strong associations between neurophysiological indicators and subjective evaluations. Conclusions: The results highlight the potential of neurophysiological measures to objectively assess learning experiences, paving the way for designing more effective and engaging learning platforms.<\/jats:p>","DOI":"10.3390\/bioengineering12060597","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T11:40:32Z","timestamp":1748864432000},"page":"597","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Multimodal Neurophysiological Approach to Evaluate Educational Contents in Terms of Cognitive Processes and Engagement"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7174-6331","authenticated-orcid":false,"given":"Vincenzo","family":"Ronca","sequence":"first","affiliation":[{"name":"Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3831-6620","authenticated-orcid":false,"given":"Pietro","family":"Aric\u00f2","sequence":"additional","affiliation":[{"name":"Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4200-9110","authenticated-orcid":false,"given":"Luca","family":"Tamborra","sequence":"additional","affiliation":[{"name":"Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonia","family":"Biagi","sequence":"additional","affiliation":[{"name":"Department of Technological Innovations and Safety of Plants, Products and Anthropic Settlements, Istituto Nazionale per L\u2019Assicurazione Contro Gli Infortuni Sul Lavoro (INAIL), 00144 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4426-051X","authenticated-orcid":false,"given":"Gianluca","family":"Di Flumeri","sequence":"additional","affiliation":[{"name":"Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1002\/wcs.1345","article-title":"Educational neuroscience: Definitional, methodological, and interpretive issues","volume":"6","author":"Byrnes","year":"2015","journal-title":"Wiley Interdiscip. Rev. Cogn. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s40593-021-00256-0","article-title":"Neurophysiological Measurements in Higher Education: A Systematic Literature Review","volume":"32","author":"Darvishi","year":"2021","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2195","DOI":"10.1016\/j.clinph.2004.06.001","article-title":"EEG source imaging","volume":"115","author":"Michel","year":"2004","journal-title":"Clin. Neurophysiol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Protzak, J., and Gramann, K. (2018). Investigating established EEG parameter during real-world driving. Front. Psychol., 9.","DOI":"10.3389\/fpsyg.2018.02289"},{"key":"ref_5","unstructured":"(2025, March 03). EEG Correlates of Task Engagement and Mental Workload in Vigilance, Learning, and Memory Tasks\u2014PubMed, Available online: https:\/\/pubmed.ncbi.nlm.nih.gov\/17547324\/."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Di Flumeri, G., Ronca, V., Giorgi, A., Vozzi, A., Aric\u00f2, P., Sciaraffa, N., Zeng, H., Dai, G., Kong, W., and Babiloni, F. (2022). EEG-Based Index for Timely Detecting User\u2019s Drowsiness Occurrence in Automotive Applications. Front. Hum. Neurosci., 16.","DOI":"10.3389\/fnhum.2022.866118"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Di Flumeri, G., Borghini, G., Aric\u00f2, P., Sciaraffa, N., Lanzi, P., Pozzi, S., Vignali, V., Lantieri, C., Bichicchi, A., and Simone, A. (2018). EEG-based mental workload neurometric to evaluate the impact of different traffic and road conditions in real driving settings. Front. Hum. Neurosci., 12.","DOI":"10.3389\/fnhum.2018.00509"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Jung, T.-P. (2011). A collaborative brain-computer interface for improving human performance. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0020422"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Wang, Q., and Zhang, L. (2021). Study of EEG characteristics while solving scientific problems with different mental effort. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-03321-9"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"119202","DOI":"10.1016\/j.neuroimage.2022.119202","article-title":"Dynamics of hidden brain states when people solve verbal puzzles","volume":"255","author":"Yu","year":"2022","journal-title":"NeuroImage"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kim, K., Duc, N.T., Choi, M., and Lee, B. (2021). EEG microstate features according to performance on a mental arithmetic task. Sci. Rep., 11.","DOI":"10.1038\/s41598-020-79423-7"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Varao-Sousa, T.L., and Kingstone, A. (2015). Memory for Lectures: How Lecture Format Impacts the Learning Experience. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0141587"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1037\/stl0000053","article-title":"Mind wandering during lectures I: Changes in rates across an entire semester","volume":"2","author":"Wammes","year":"2016","journal-title":"Sch. Teach. Learn. Psychol."},{"key":"ref_14","unstructured":"Munoz, D.A., and Tucker, C.S. (2014, January 17\u201320). Assessing Students\u2019 Emotional States: An Approach to Identify Lectures That Provide an Enhanced Learning Experience. Proceedings of the ASME Design Engineering Technical Conference, Buffalo, NY, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14819","DOI":"10.1109\/ACCESS.2017.2731784","article-title":"An EEG-Based Cognitive Load Assessment in Multimedia Learning Using Feature Extraction and Partial Directed Coherence","volume":"5","author":"Mazher","year":"2017","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1016\/j.compedu.2012.05.006","article-title":"Assessing the effects of different multimedia materials on emotions and learning performance for visual and verbal style learners","volume":"59","author":"Chen","year":"2012","journal-title":"Comput. Educ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Di Flumeri, G., Giorgi, A., Germano, D., Ronca, V., Vozzi, A., Borghini, G., Tamborra, L., Simonetti, I., Capotorto, R., and Ferrara, S. (2023). A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees. Sensors, 23.","DOI":"10.3390\/s23208389"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8259","DOI":"10.1007\/s11042-022-12296-2","article-title":"Split-attention effects in multimedia learning environments: Eye-tracking and EEG analysis","volume":"81","author":"Ozel","year":"2022","journal-title":"Multimedia Tools Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"16261","DOI":"10.1007\/s11042-018-7016-z","article-title":"EEG in classroom: EMD features to detect situational interest of students during learning","volume":"78","author":"Babiker","year":"2018","journal-title":"Multimedia Tools Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bashir, F., Ali, A., Soomro, T.A., Marouf, M., Bilal, M., and Chowdhry, B.S. (2021). Electroencephalogram (EEG) Signals for Modern Educational Research. Innovative Education Technologies for 21st Century Teaching and Learning, CRC Press.","DOI":"10.1201\/9781003143796-10"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Simonetti, I., Tamborra, L., Giorgi, A., Ronca, V., Vozzi, A., Aric\u00f2, P., Borghini, G., Sciaraffa, N., Trettel, A., and Babiloni, F. (2023). Neurophysiological Evaluation of Students\u2019 Experience during Remote and Face-to-Face Lessons: A Case Study at Driving School. Brain Sci., 13.","DOI":"10.3390\/brainsci13010095"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111555","DOI":"10.1016\/j.buildenv.2024.111555","article-title":"A comprehensive review of research on indoor cognitive performance using electroencephalogram technology","volume":"257","author":"Zhang","year":"2024","journal-title":"Build. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yuvaraj, R., Chadha, S., Prince, A.A., Murugappan, M., Bin Islam, S., Sumon, S.I., and Chowdhury, M.E.H. (2024). A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns. Algorithms, 17.","DOI":"10.3390\/a17110503"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1177\/1477878515606621","article-title":"Is learning styles-based instruction effective? A comprehensive analysis of recent research on learning styles","volume":"13","author":"Cuevas","year":"2015","journal-title":"Theory Res. Educ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"85151","DOI":"10.1109\/ACCESS.2021.3085844","article-title":"Blended Learning Tools and Practices: A Comprehensive Analysis","volume":"9","author":"Kumar","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ronca, V., Brambati, F., Napoletano, L., Marx, C., Tr\u00f6sterer, S., Vozzi, A., Aric\u00f2, P., Giorgi, A., Capotorto, R., and Borghini, G. (2024). A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions. Brain Sci., 14.","DOI":"10.3390\/brainsci14030193"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Apicella, A., Arpaia, P., Frosolone, M., Improta, G., Moccaldi, N., and Pollastro, A. (2022). EEG-based measurement system for monitoring student engagement in learning 4.0. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-09578-y"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rai, L., Lee, H., Becke, E., Trenado, C., Abad-Hernando, S., Sperling, M., Vidaurre, D., Wald-Fuhrmann, M., Richardson, D.C., and Ward, J.A. (2024, May 05). Delta-Band Inter-Brain Synchrony Reflects Collective Audience Engagement with Live Dance Performances. Available online: https:\/\/osf.io\/uz573.","DOI":"10.2139\/ssrn.4931648"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sciaraffa, N., Di Flumeri, G., Germano, D., Giorgi, A., Di Florio, A., Borghini, G., Vozzi, A., Ronca, V., Babiloni, F., and Aric\u00f2, P. (2022). Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces. Front. Hum. Neurosci., 16.","DOI":"10.3389\/fnhum.2022.901387"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"056023","DOI":"10.1088\/1741-2552\/ad7b78","article-title":"o-CLEAN: A novel multi-stage algorithm for the ocular artifacts\u2019 correction from EEG data in out-of-the-lab applications","volume":"21","author":"Ronca","year":"2024","journal-title":"J. Neural Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/S0165-0173(98)00056-3","article-title":"EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis","volume":"29","author":"Klimesch","year":"1999","journal-title":"Brain Res. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.ijpsycho.2005.03.018","article-title":"Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory","volume":"57","author":"Sauseng","year":"2005","journal-title":"Int. J. Psychophysiol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Vecchiato, G., Babiloni, F., Astolfi, L., Toppi, J., Cherubino, P., Dai, J., Kong, W., and Wei, D. (2011, January 15\u201317). Enhance of theta EEG spectral activity related to the memorization of commercial advertisings in Chinese and Italian subjects. Proceedings of the 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), Shanghai, China.","DOI":"10.1109\/BMEI.2011.6098615"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.tics.2012.10.007","article-title":"Alpha-band oscillations, attention, and controlled access to stored information","volume":"16","author":"Klimesch","year":"2012","journal-title":"Trends Cogn. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Babiloni, F. (2019). Mental Workload Monitoring: New Perspectives from Neuroscience. Communications in Computer and Information Science, Springer.","DOI":"10.1007\/978-3-030-32423-0_1"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/B978-0-444-63934-9.00015-9","article-title":"Monitoring performance of professional and occupational operators","volume":"Volume 168","author":"Borghini","year":"2020","journal-title":"Handbook of Clinical Neurology"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/00140139.2014.956151","article-title":"State of science: Mental workload in ergonomics","volume":"58","author":"Young","year":"2015","journal-title":"Ergonomics"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1177\/1087054712460087","article-title":"A Decade of EEG Theta\/Beta Ratio Research in ADHD: A Meta-Analysis","volume":"17","author":"Arns","year":"2013","journal-title":"J. Atten. Disord."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Heinrich, H., Busch, K., Studer, P., Erbe, K., Moll, G.H., and Kratz, O. (2014). EEG spectral analysis of attention in ADHD: Implications for neurofeedback training?. Front. Hum. Neurosci., 8.","DOI":"10.3389\/fnhum.2014.00611"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1109\/TNSRE.2022.3154369","article-title":"Time-Distributed Attention Network for EEG-Based Motor Imagery Decoding From the Same Limb","volume":"30","author":"Ma","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabilitation Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"598","DOI":"10.3758\/s13415-015-0351-x","article-title":"Spontaneous EEG theta\/beta ratio and delta\u2013beta coupling in relation to attentional network functioning and self-reported attentional control","volume":"15","author":"Bornas","year":"2015","journal-title":"Cogn. Affect. Behav. Neurosci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Di Flumeri, G., De Crescenzio, F., Berberian, B., Ohneiser, O., Kramer, J., Aric\u00f2, P., Borghini, G., Babiloni, F., Bagassi, S., and Piastra, S. (2019). Brain\u2013Computer Interface-Based Adaptive Automation to Prevent Out-Of-The-Loop Phenomenon in Air Traffic Controllers Dealing With Highly Automated Systems. Front. Hum. Neurosci., 13.","DOI":"10.3389\/fnhum.2019.00296"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Kumada, T. (2017). Relationship between workload and mind-wandering in simulated driving. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176962"},{"key":"ref_45","unstructured":"Smallwood, J. (2015). Mind wandering and attention. The Handbook of Attention, Boston Review."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A Real-Time QRS Detection Algorithm","volume":"32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_47","unstructured":"Ramshur, J. (2025, March 03). Design, Evaluation, and Application of Heart Rate Variability Analysis Software (HRVAS). Electronic Theses and Dissertations. Available online: https:\/\/digitalcommons.memphis.edu\/etd\/83."},{"key":"ref_48","first-page":"647","article-title":"Decomposition of skin conductance data by means of nonnegative deconvolution","volume":"47","author":"Benedek","year":"2010","journal-title":"Psychophysiology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12909-023-04820-1","article-title":"Construct validity of a questionnaire for measuring student engagement in problem-based learning tutorials","volume":"23","author":"Kassab","year":"2023","journal-title":"BMC Med. Educ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1080\/135383203200015111","article-title":"The Development of an Extended Course Experience Questionnaire","volume":"9","author":"Griffin","year":"2003","journal-title":"Qual. High. Educ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1007\/s00180-021-01188-y","article-title":"Shapiro-Wilk test for multivariate skew-normality","volume":"37","year":"2022","journal-title":"Comput. Stat."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Bakdash, J.Z., and Marusich, L.R. (2017). Repeated measures correlation. Front. Psychol., 8.","DOI":"10.3389\/fpsyg.2017.00456"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zivan, M., Vaknin, S., Peleg, N., Ackerman, R., and Horowitz-Kraus, T. (2023). Higher theta-beta ratio during screen-based vs. printed paper is related to lower attention in children: An EEG study. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0283863"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Jab\u00e8s, A., Klencklen, G., Ruggeri, P., Antonietti, J.-P., Lavenex, P.B., and Lavenex, P. (2021). Age-Related Differences in Resting-State EEG and Allocentric Spatial Working Memory Performance. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.704362"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ronca, V., Di Flumeri, G., Vozzi, A., Giorgi, A., Arico, P., Sciaraffa, N., Babiloni, F., and Borghini, G. (2022, January 11\u201315). Validation of an EEG-based Neurometric for online monitoring and detection of mental drowsiness while driving. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, UK.","DOI":"10.1109\/EMBC48229.2022.9871505"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ronca, V., Martinez-Levy, A.C., Vozzi, A., Giorgi, A., Aric\u00f2, P., Capotorto, R., Borghini, G., Babiloni, F., and Di Flumeri, G. (2023). Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3+. Sensors, 23.","DOI":"10.20944\/preprints202306.0378.v1"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Capotorto, R., Ronca, V., Sciaraffa, N., Borghini, G., Di Flumeri, G., Mezzadri, L., Vozzi, A., Giorgi, A., Germano, D., and Babiloni, F. (2024). Cooperation objective evaluation in aviation: Validation and comparison of two novel approaches in simulated environment. Front. Neurosci., 18.","DOI":"10.3389\/fninf.2024.1409322"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ronca, V., Uflaz, E., Turan, O., Bantan, H., MacKinnon, S.N., Lommi, A., Pozzi, S., Kurt, R.E., Arslan, O., and Kurt, Y.B. (2023). Neurophysiological Assessment of An Innovative Maritime Safety System in Terms of Ship Operators\u2019 Mental Workload, Stress, and Attention in the Full Mission Bridge Simulator. Brain Sci., 13.","DOI":"10.3390\/brainsci13091319"},{"key":"ref_59","unstructured":"Khanal, S., and Pokhrel, S.R. (2024). Analysis, Modeling and Design of Personalized Digital Learning Environment. arXiv, Available online: https:\/\/arxiv.org\/abs\/2405.10476v1."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s11528-022-00802-0","article-title":"The Charm or Chasm of Digital Personalized Learning in Education: Teachers\u2019 Reported Use, Perceptions and Expectations","volume":"67","author":"Elen","year":"2023","journal-title":"TechTrends"},{"key":"ref_61","unstructured":"(2020). Technologies and Tools for Creating Adaptive E-Learning Content. \u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u043a\u0430 \u0438 \u0438\u043d\u0444o\u0440\u043c\u0430\u0442\u0438\u043a\u0430, 63, 382\u2013390."},{"key":"ref_62","first-page":"18","article-title":"From Digital Learning Resources to Adaptive Learning Objects: An Overview","volume":"1126","author":"Apoki","year":"2020","journal-title":"Commun. Comput. Inf. Sci."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/6\/597\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:44:55Z","timestamp":1760031895000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/6\/597"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,31]]},"references-count":62,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["bioengineering12060597"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12060597","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,31]]}}}