Real-time emotion classification using EEG data stream in e-learning contexts
| العنوان: | Real-time emotion classification using EEG data stream in e-learning contexts |
|---|---|
| المؤلفون: | Santi Fort, Laia Subirats, Fatos Xhafa, Arijit Nandi |
| المساهمون: | Universitat Politècnica de Catalunya. Doctorat en Intel·ligència Artificial, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. IMP - Information Modeling and Processing |
| المصدر: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Sensors Volume 21 Issue 5 Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 1589, p 1589 (2021) |
| بيانات النشر: | Multidisciplinary Digital Publishing Institute (MDPI), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors [Àrees temàtiques de la UPC], Computer science, E-learning (theory), Emotions, Logistic regression, 02 engineering and technology, real-time emotion classification, lcsh:Chemical technology, computer.software_genre, E-learning, Online training, Biochemistry, Web-based instruction, Analytical Chemistry, Machine Learning, Stochastic gradient descent, stochastic gradient descent, 0202 electrical engineering, electronic engineering, information engineering, lcsh:TP1-1185, Instrumentation, 05 social sciences, 050301 education, Electroencephalography, Atomic and Molecular Physics, and Optics, emotion classification, Emotion classification, 020201 artificial intelligence & image processing, Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC], Mineria de dades, Algorithms, online training, Internet of things, Internet de les coses, Context (language use), Machine learning, Article, Education, Distance, Humans, Electrical and Electronic Engineering, Set (psychology), Data mining, e-learning, business.industry, Emotional intelligence, Deep learning, logistic regression, Ensenyament virtual, Data set, Real-time emotion classification, Artificial intelligence, business, 0503 education, computer, Computer-Assisted Instruction |
| الوصف: | In face-to-face and online learning, emotions and emotional intelligence have an influence and play an essential role. Learners’ 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::433447264408ecc77d974e8192f4f746 http://hdl.handle.net/2117/344002 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....433447264408ecc77d974e8192f4f746 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |