{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T06:18:13Z","timestamp":1783491493954,"version":"3.55.0"},"reference-count":63,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n","doi-asserted-by":"publisher","award":["PID2020-117111RB-I00"],"award-info":[{"award-number":["PID2020-117111RB-I00"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) and machine learning (ML) are increasingly applied in psychology, particularly in educational and clinical settings, to analyse complex behavioural and physiological data. However, evidence regarding the capacity of multimodal physiological signals to characterise cognitive and emotional processes in ecologically valid higher education environments remains limited. This study explored the potential of supervised and unsupervised ML approaches to analyse multimodal physiological responses elicited by emotional avatars in a real educational context.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A convenience sample of 55 participants, including university students and lecturers, was recruited. After data cleaning, the final sample comprised 48 participants. Electroencephalography (EEG) and galvanic skin response (GSR) signals were recorded while participants observed avatars displaying different emotional states. Event-related potential (ERP)-derived features were extracted from EEG recordings. Supervised learning was conducted using a Random Forest classifier to evaluate the predictive contribution of physiological, subjective (NASA-TLX), and sociodemographic variables in classifying professional category. In addition, unsupervised analyses were performed using k-means clustering and principal component analysis (PCA) to identify latent patterns within the physiological data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Sociodemographic variables achieved the highest predictive performance, followed by subjective workload measures, whereas physiological features demonstrated more limited discriminative power. Cluster analysis revealed substantial overlap among participant response profiles, and PCA showed limited separation between groups in the reduced-dimensional space. These findings suggest that physiological responses associated with emotional processing are better represented as continuous patterns of variability rather than as clearly differentiated categories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings highlight both the opportunities and limitations of applying ML techniques to multimodal physiological data in ecologically valid educational settings. Although physiological measures alone showed limited predictive capacity, they provided complementary information regarding cognitive and emotional processes. Furthermore, the study proposes a dashboard-based framework for the automated integration, analysis, and visualisation of multimodal data, supporting future developments in educational psychology, affective computing, and precision psychology. Overall, the results underscore the need for larger samples and more advanced multimodal modelling strategies to improve the interpretability and predictive value of physiological signals in real-world contexts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1835911","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:54:15Z","timestamp":1783490055000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Emotion recognition from multimodal biosignals: supervised and unsupervised machine learning approaches based on EEG and GSR"],"prefix":"10.3389","volume":"17","author":[{"given":"Mar\u00eda Consuelo","family":"S\u00e1iz-Manzanares","sequence":"first","affiliation":[{"name":"GIR DATAHES, Consolidated Research Unit N.\u00b0 348 JCYL, Health Sciences Department, Universidad de Burgos","place":["Burgos, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ra\u00fal","family":"Marticorena-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"GIR ADMIRABLE, Consolidated Research Unit N.\u00b0 170 JCYL, Computer Engineering Department, Universidad de Burgos","place":["Burgos, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s11409-020-09231-x","article-title":"Reflections on the field of metacognition: issues, challenges, and opportunities","volume":"15","author":"Azevedo","year":"2020","journal-title":"Metacogn. Learn."},{"key":"ref2","article-title":"M2LADS: a system for generating multimodal learning analytics dashboards in open education","volume-title":"arXiv","author":"Becerra","year":"2023"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/s44217-025-00964-y","article-title":"AI-powered learning analytics dashboards: a systematic review of applications, techniques, and research gaps","volume":"4","author":"Cabral","year":"2025","journal-title":"Discov. Educ."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/s12528-019-09225-y","article-title":"Process mining for self-regulated learning assessment in e-learning","volume":"32","author":"Cerezo","year":"2020","journal-title":"J. Comput. High. Educ."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"100602","DOI":"10.1016\/j.patter.2022.100602","article-title":"Modern views of machine learning for precision psychiatry","volume":"3","author":"Chen","year":"2022","journal-title":"Patterns"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"104593","DOI":"10.1016\/j.actpsy.2024.104593","article-title":"The revolution of generative artificial intelligence in psychology: the interweaving of behavior, consciousness, and ethics","volume":"251","author":"Chen","year":"2024","journal-title":"Acta Psychol."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1007\/s11251-025-09729-4","article-title":"Novice and expert self-regulated learning phase transitions in medical diagnosis: implications for adaptive and intelligent systems","volume":"53","author":"Cloude","year":"2025","journal-title":"Instr. Sci."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"e18","DOI":"10.1017\/S0033291724003295","article-title":"Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications","volume":"55","author":"Cruz-Gonz\u00e1lez","year":"2025","journal-title":"Psychol. Med."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15391523.2025.2586570","article-title":"Investigating the impacts of stealth assessment on physiological arousal during game-based learning","volume":"25","author":"Dever","year":"2025","journal-title":"J. Res. Technol. Educ."},{"key":"ref11","article-title":"Towards a rigorous science of interpretable machine learning","volume-title":"arXiv","author":"Doshi-Velez","year":"2017"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"4841","DOI":"10.1038\/s41598-023-31909-w","article-title":"Using dimensionality-reduction techniques to understand the organization of psychotic symptoms in persistent psychotic illness and first episode psychosis","volume":"13","author":"Fleming","year":"2023","journal-title":"Sci. Rep."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"5865","DOI":"10.1109\/JBHI.2024.3419043","article-title":"Cross-modal guiding neural network for multimodal emotion recognition from EEG and eye movement signals","volume":"28","author":"Fu","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"115265","DOI":"10.1016\/j.psychres.2023.115265","article-title":"An overview of clustering methods with guidelines for application in mental health research","volume":"327","author":"Gao","year":"2023","journal-title":"Psychiatry Res."},{"key":"ref15","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-10247-4","volume-title":"Data preprocessing in data mining","author":"Garc\u00eda","year":"2015"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"e31279","DOI":"10.14201\/eks.31279","article-title":"La percepci\u00f3n de la Inteligencia Artificial en contextos educativos tras el lanzamiento de ChatGPT: disrupci\u00f3n o p\u00e1nico","volume":"24","author":"Garc\u00eda-Pe\u00f1alvo","year":"2023","journal-title":"Educ. Knowl. Soc."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"942","DOI":"10.14201\/eks.31942","article-title":"Inteligencia artificial generativa y educaci\u00f3n: Un an\u00e1lisis desde m\u00faltiples perspectivas","volume":"25","author":"Garc\u00eda-Pe\u00f1alvo","year":"2024","journal-title":"Educ. i Knowl. Soc."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1080\/14639220210159717","article-title":"Neurophysiological measures of cognitive workload during human-computer interaction","volume":"4","author":"Gevins","year":"2003","journal-title":"Theor. Issues Ergon. Sci."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.sleep.2023.01.002","article-title":"Predicting depressive symptoms in middle-aged and elderly adults using sleep data and clinical health markers: a machine learning approach","volume":"102","author":"Gomes","year":"2023","journal-title":"Sleep Med."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"104278","DOI":"10.1016\/j.dsp.2023.104278","article-title":"Emotion recognition from multiple physiological signals using intra- and inter-modality attention fusion network","volume":"144","author":"Gong","year":"2023","journal-title":"Digit. Signal Process."},{"key":"ref21","first-page":"139","volume-title":"Human Mental Workload","author":"Hart","year":"1988"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning","author":"Hastie","year":"2009"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"2714","DOI":"10.3390\/s25092714","article-title":"Advances in wearable sensors for learning analytics: trends, challenges, and prospects","volume":"25","author":"Hong","year":"2025","journal-title":"Sensors"},{"key":"ref24","doi-asserted-by":"crossref","DOI":"10.1002\/0471221317","volume-title":"Independent Component Analysis","author":"Hyv\u00e4rinen","year":"2001"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/SoutheastCon51012.2023.10115167","volume-title":"SoutheastCon 2023","author":"Jamal","year":"2023"},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-0716-1418-1","volume-title":"An Introduction to Statistical Learning: with Applications in R","author":"James","year":"2021"},{"key":"ref27","article-title":"Chatting with a learning analytics dashboard: the role of generative AI literacy","volume-title":"arXiv","author":"Jin","year":"2024"},{"key":"ref28","volume-title":"Big data in cognitive science","author":"Jones","year":"2017"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"104756","DOI":"10.1016\/j.compedu.2023.104756","article-title":"Will, skills, or conscientiousness: what predicts teachers\u2019 intentions to participate in technology-related professional development?","volume":"198","author":"Kaliisa","year":"2023","journal-title":"Comput. Educ."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: a database for emotion analysis using physiological signals","volume":"3","author":"Koelstra","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1080\/00273171.2024.2432918","article-title":"A gentle introduction and application of feature-based clustering with psychological time series","volume":"60","author":"Kreienkamp","year":"2025","journal-title":"Multivar. Behav. Res."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1023\/A:1016304305535","article-title":"Discretization: an enabling technique","volume":"6","author":"Liu","year":"2002","journal-title":"Data Min. Knowl. Discov."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref34","first-page":"281","author":"MacQueen","year":"1967"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"164","DOI":"10.36825\/RITI.12.26.013","article-title":"Learning analytics dashboard to support programming instruction in distance education","volume":"12","author":"Mart\u00ednez","year":"2024","journal-title":"Rev. Investig. Tecnol. Inform."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"36310","DOI":"10.35699\/1983-3652.2022.36310","article-title":"Modelos predictivos basados en uso de anal\u00edticas de aprendizaje en educaci\u00f3n superior: una revisi\u00f3n sistem\u00e1tica","volume":"15","author":"Mella Norambuena","year":"2022","journal-title":"Texto Livre"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1109\/TAFFC.2018.2884461","article-title":"AMIGOS: a dataset for affect, personality and mood research on individuals and groups","volume":"12","author":"Miranda-Correa","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref38","volume-title":"Machine Learning","author":"Mitchell","year":"1997"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1101\/2021.06.30.450026","article-title":"Reliability of decision-making and reinforcement learning computational parameters","author":"Mkrtchian","year":"2021","journal-title":"Comput. Psychiatry"},{"key":"ref40","volume-title":"Introduction to Linear Regression Analysis","author":"Montgomery","year":"2012"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.chb.2018.06.028","article-title":"Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning","volume":"96","author":"Mudrick","year":"2019","journal-title":"Comput. Hum. Behav."},{"key":"ref42","volume-title":"Machine Learning: A Probabilistic Perspective","author":"Murphy","year":"2012"},{"key":"ref43","volume-title":"Artificial Intelligence: A New Synthesis","author":"Nilsson","year":"1998"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"207","DOI":"10.3758\/s13414-024-02917-3","article-title":"Combining EEG and eye-tracking in virtual reality: obtaining fixation-onset event-related potentials and event-related spectral perturbations","volume":"87","author":"Nolte","year":"2025","journal-title":"Atten. Percept. Psychophys."},{"key":"ref45","first-page":"150","author":"Pinargote","year":"2024"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"e75238","DOI":"10.2196\/75238","article-title":"Applications of artificial intelligence in psychiatry and psychology education: a scoping review","volume":"11","author":"Pr\u00e9gent","year":"2025","journal-title":"JMIR Med. Educ."},{"key":"ref47","doi-asserted-by":"publisher","first-page":"106553","DOI":"10.1016\/j.bspc.2024.106553","article-title":"Multimodal machine learning approach for emotion recognition using physiological signals","volume":"96","author":"Ramadan","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref48","volume-title":"Artificial Intelligence: A Modern Approach","author":"Russell","year":"2021"},{"key":"ref49","author":"S\u00e1iz-Manzanares","year":"2024"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"7017","DOI":"10.3390\/app14167017","article-title":"Using integrated multimodal technology: a way to personalised learning in health sciences and biomedical engineering students","volume":"14","author":"S\u00e1iz-Manzanares","year":"2024","journal-title":"Appl. Sci."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"101938","DOI":"10.1016\/j.tsc.2025.101938","article-title":"Monitoring university students\u2019 learning processes: application of advanced learning technologies and integrated multichannel techniques","volume":"58","author":"S\u00e1iz-Manzanares","year":"2025","journal-title":"Think. Skills Creat."},{"key":"ref52","doi-asserted-by":"publisher","first-page":"1737723","DOI":"10.3389\/fnhum.2026.1737723","article-title":"Processing and analysis of portable EEG data for cognitive load assessment in neurotypical university students","volume":"20","author":"S\u00e1iz-Manzanares","year":"2026","journal-title":"Front. Hum. Neurosci."},{"key":"ref53","doi-asserted-by":"publisher","first-page":"6157","DOI":"10.3390\/app11136157","article-title":"Analysis of the learning process through eye tracking technology and feature selection techniques","volume":"11","author":"S\u00e1iz-Manzanares","year":"2021","journal-title":"Appl. Sci."},{"key":"ref54","doi-asserted-by":"publisher","first-page":"23661","DOI":"10.1007\/s11042-024-20016-1","article-title":"Generative artificial intelligence: a systematic review and applications","volume":"84","author":"Sengar","year":"2025","journal-title":"Multimed. Tools Appl."},{"key":"ref55","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s41239-021-00313-7","article-title":"Learning analytics dashboard: a tool for providing actionable insights to learners","volume":"19","author":"Susnjak","year":"2022","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref56","first-page":"137","author":"Tarnowski","year":"2018"},{"key":"ref57","doi-asserted-by":"publisher","first-page":"3129","DOI":"10.1038\/s41380-022-01635-2","article-title":"Predicting the future of neuroimaging predictive models in mental health","volume":"27","author":"Tejavibulya","year":"2022","journal-title":"Mol. Psychiatry"},{"key":"ref58","doi-asserted-by":"publisher","first-page":"14279","DOI":"10.1007\/s10639-023-12401-4","article-title":"Learning analytics dashboards are increasingly becoming about learning and not just analytics","volume":"29","author":"Verbert","year":"2024","journal-title":"Educ. Inf. Technol."},{"key":"ref60","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1109\/TAFFC.2025.3618639","article-title":"Hybrid-supervised hypergraph-enhanced transformer for micro-gesture based emotion recognition","volume":"17","author":"Xia","year":"2026","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref61","doi-asserted-by":"publisher","first-page":"8590","DOI":"10.1109\/TIP.2020.3011234","article-title":"Revealing the invisible with model and data shrinking for composite-database micro-expression recognition","volume":"29","author":"Xia","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref62","doi-asserted-by":"publisher","first-page":"5040","DOI":"10.1109\/EMBC.2014.6944757","article-title":"Multimodal emotion recognition using EEG and eye tracking data","volume":"2014","author":"Zheng","year":"2014","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref63","doi-asserted-by":"publisher","first-page":"1137010","DOI":"10.3389\/fpsyg.2023.1137010","article-title":"Emotions in self-regulated learning: a critical literature review and meta-analysis","volume":"14","author":"Zheng","year":"2023","journal-title":"Front. Psychol."},{"key":"ref64","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev."}],"container-title":["Frontiers in Psychology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyg.2026.1835911\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:54:19Z","timestamp":1783490059000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyg.2026.1835911\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,8]]},"references-count":63,"alternative-id":["10.3389\/fpsyg.2026.1835911"],"URL":"https:\/\/doi.org\/10.3389\/fpsyg.2026.1835911","relation":{},"ISSN":["1664-1078"],"issn-type":[{"value":"1664-1078","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,8]]},"article-number":"1835911"}}