{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:08:05Z","timestamp":1775592485639,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002809","name":"Generalitat de Catalunya","doi-asserted-by":"publisher","award":["ACCI\u00d3 (Pla d\u2019Actuaci\u00f3 de Centres Tecnol\u00f2gics 488 2019) under the project Augmented Workplace."],"award-info":[{"award-number":["ACCI\u00d3 (Pla d\u2019Actuaci\u00f3 de Centres Tecnol\u00f2gics 488 2019) under the project Augmented Workplace."]}],"id":[{"id":"10.13039\/501100002809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In face-to-face and online learning, emotions and emotional intelligence have an influence and play an essential role. Learners\u2019 emotions are crucial for e-learning system because they promote or restrain the learning. Many researchers have investigated the impacts of emotions in enhancing and maximizing e-learning outcomes. Several machine learning and deep learning approaches have also been proposed to achieve this goal. All such approaches are suitable for an offline mode, where the data for emotion classification are stored and can be accessed infinitely. However, these offline mode approaches are inappropriate for real-time emotion classification when the data are coming in a continuous stream and data can be seen to the model at once only. We also need real-time responses according to the emotional state. For this, we propose a real-time emotion classification system (RECS)-based Logistic Regression (LR) trained in an online fashion using the Stochastic Gradient Descent (SGD) algorithm. The proposed RECS is capable of classifying emotions in real-time by training the model in an online fashion using an EEG signal stream. To validate the performance of RECS, we have used the DEAP data set, which is the most widely used benchmark data set for emotion classification. The results show that the proposed approach can effectively classify emotions in real-time from the EEG data stream, which achieved a better accuracy and F1-score than other offline and online approaches. The developed real-time emotion classification system is analyzed in an e-learning context scenario.<\/jats:p>","DOI":"10.3390\/s21051589","type":"journal-article","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T04:36:24Z","timestamp":1614314184000},"page":"1589","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4238-5183","authenticated-orcid":false,"given":"Arijit","family":"Nandi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Universitat Polit\u00e8cnica de Catalunya (BarcelonaTech), 08034 Barcelona, Spain"},{"name":"Eurecat, Centre Tecnol\u00f2gic de Catalunya, 08005 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6569-5497","authenticated-orcid":false,"given":"Fatos","family":"Xhafa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universitat Polit\u00e8cnica de Catalunya (BarcelonaTech), 08034 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8646-5463","authenticated-orcid":false,"given":"Laia","family":"Subirats","sequence":"additional","affiliation":[{"name":"Eurecat, Centre Tecnol\u00f2gic de Catalunya, 08005 Barcelona, Spain"},{"name":"ADaS Lab, Universitat Oberta de Catalunya, 08018 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2189-6830","authenticated-orcid":false,"given":"Santi","family":"Fort","sequence":"additional","affiliation":[{"name":"Eurecat, Centre Tecnol\u00f2gic de Catalunya, 08005 Barcelona, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1111\/j.1464-0597.1992.tb00712.x","article-title":"The Impact of Emotions on Learning and Achievement: Towards a Theory of Cognitive\/Motivational Mediators","volume":"41","author":"Pekrun","year":"1992","journal-title":"Appl. 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