EduEmotion: An EEG Dataset of Emotional Responses in Simulated Learning Tasks
Resumo
Este artigo apresenta o EduEmotion, um conjunto de dados baseado em EEG voltado ao reconhecimento de emoções na educação a distância. A base registra dinâmicas afetivas durante interações digitais neutras e autoguiadas, representativas de experiências comuns de aprendizagem online. Reúne sinais de EEG e autorrelatos emocionais de 22 participantes. O estudo valoriza a representatividade das respostas emocionais em cenários educacionais autênticos e a eliminação de viés disciplinar. Os resultados indicam padrões consistentes de engajamento e frustração leve — estados típicos da aprendizagem remota — fornecendo uma base realista para o desenvolvimento de tecnologias educacionais adaptativas.Referências
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Bai, X. and Liu, W. (2024). Cognitive load and motivation in online learning: A systematic review. International Journal of Educational Technology in Higher Education, 21(1):3.
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Coan, J. A. and Allen, J. J. (2004). Frontal eeg asymmetry as a moderator and mediator of emotion. Biological Psychology, 67(1-2):7–49.
D’Mello, S. and Graesser, A. (2021). Emotions and education: The interplay of cognition, motivation, and affect. Educational Psychologist, 56(2):93–111.
Gupta, R., Nagananda, K., Balasubramanian, V., and Mitra, K. (2016). Daisee: Towards user engagement recognition in the wild. In Proceedings of the 18th ACM International Conference on Multimodal Interaction, pages 297–304. ACM.
Holmes, W. and Bialik, M. (2022). The roles of emotions in ai-based learning: A critical overview. British Journal of Educational Technology, 53(4):587–603.
Inc., E. (2013). EEG headset and method of manufacturing same. US Patent 8,543,180.
Jenke, R., Peer, A., and Buss, M. (2014). Feature Extraction and Selection for Emotion Recognition from EEG. IEEE Transactions on Affective Computing, 5(3):327–339.
Koelstra, S., Mühl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., and Patras, I. (2012). DEAP: A Database for Emotion Analysis using Physiological Signals. IEEE Transactions on Affective Computing, 3(1):18–31.
Lin, Y., Duann, J., and Jung, T. (2019a). Eeg-based classification of positive and negative emotional states using recurrent neural networks. IEEE Transactions on Affective Computing, 10(2):337–350.
Lin, Y.-P., Duann, J.-R., and Jung, T.-P. (2019b). EEG-based Classification of Positive and Negative Emotional States Using Recurrent Neural Networks. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), pages 399–405.
Lin, Y.-P., Wang, C.-H., Jung, T.-P., Wu, T.-L., Jeng, S.-K., Duann, J.-R., and Chen, J.-H. (2010). EEG-based emotion recognition in music listening. IEEE Transactions on Biomedical Engineering, 57(7):1798–1806.
McDaniel, B. T., Higgins, T. E., and Olsen, S. A. (2018). Facial expression recognition in educational technology: A systematic review. In Proceedings of the 18th International Conference on Educational Data Mining, pages 45–53.
Mendoza, F., Menezes, M., Sant’Anna, A., Ortiz Barrios, M., Samara, A., and Galway, L. (2019). Affective recognition from eeg signals: an integrated data-mining approach. Journal of Ambient Intelligence and Humanized Computing, 10.
Menezes, M. L. R., Samara, A., Galway, L., Sant’Anna, A., Verikas, A., Alonso-Fernandez, F., Wang, H., and Bond, R. (2017). Towards emotion recognition for virtual environments: An evaluation of EEG features on benchmark dataset. Personal and Ubiquitous Computing, 21(6):1003–1013.
Miranda-Correa, J., Abadi, M. K., Sebe, N., and Patras, I. (2021). Amigos: A dataset for affect, personality and mood research on individuals and groups. IEEE Transactions on Affective Computing, 12(2):479–493.
Murugappan, M., Ramachandran, N., and Sazali, Y. (2010). Classification of human emotion from EEG using discrete wavelet transform. Journal of Biomedical Science and Engineering, 3(4):390–396.
Picard, R. W. (1997). Affective Computing. MIT Press, Cambridge, MA, USA.
Rodríguez, A., Ocampo, M., and D’Mello, S. (2020). Improving engagement detection in e-learning contexts with advanced eeg features. In Proceedings of the 20th International Conference on Artificial Intelligence in Education (AIED), pages 321–331.
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6):1161–1178.
Sun, Y. and Li, C. (2022). Posture-based engagement detection for online learners. In Proceedings of the 22nd International Conference on Artificial Intelligence in Education (AIED), pages 210–221.
Woolf, B. P. and Burleson, W. (2019). Affect-aware learning technologies: From design to research. International Journal of Artificial Intelligence in Education, 29(3):371–381.
Zhang, Z., Wang, Y., Cai, J., and Hu, W. (2020). EEG-based Attention Monitoring and Task Difficulty Analysis in E-Learning. Computers & Education, 151:103832.
Zheng, W.-L., Zhu, J., and Lu, B.-L. (2015). Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks. IEEE Transactions on Autonomous Mental Development, 7(3):162–175.
Zhou, Y. and Kim, S. (2023). Exploring the relationships between student engagement, motivation, and performance in online learning environments. Humanities and Social Sciences Communications, 10(1):47.
Bai, X. and Liu, W. (2024). Cognitive load and motivation in online learning: A systematic review. International Journal of Educational Technology in Higher Education, 21(1):3.
Bradley, M. M. and Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1):49–59.
Coan, J. A. and Allen, J. J. (2004). Frontal eeg asymmetry as a moderator and mediator of emotion. Biological Psychology, 67(1-2):7–49.
D’Mello, S. and Graesser, A. (2021). Emotions and education: The interplay of cognition, motivation, and affect. Educational Psychologist, 56(2):93–111.
Gupta, R., Nagananda, K., Balasubramanian, V., and Mitra, K. (2016). Daisee: Towards user engagement recognition in the wild. In Proceedings of the 18th ACM International Conference on Multimodal Interaction, pages 297–304. ACM.
Holmes, W. and Bialik, M. (2022). The roles of emotions in ai-based learning: A critical overview. British Journal of Educational Technology, 53(4):587–603.
Inc., E. (2013). EEG headset and method of manufacturing same. US Patent 8,543,180.
Jenke, R., Peer, A., and Buss, M. (2014). Feature Extraction and Selection for Emotion Recognition from EEG. IEEE Transactions on Affective Computing, 5(3):327–339.
Koelstra, S., Mühl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., and Patras, I. (2012). DEAP: A Database for Emotion Analysis using Physiological Signals. IEEE Transactions on Affective Computing, 3(1):18–31.
Lin, Y., Duann, J., and Jung, T. (2019a). Eeg-based classification of positive and negative emotional states using recurrent neural networks. IEEE Transactions on Affective Computing, 10(2):337–350.
Lin, Y.-P., Duann, J.-R., and Jung, T.-P. (2019b). EEG-based Classification of Positive and Negative Emotional States Using Recurrent Neural Networks. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), pages 399–405.
Lin, Y.-P., Wang, C.-H., Jung, T.-P., Wu, T.-L., Jeng, S.-K., Duann, J.-R., and Chen, J.-H. (2010). EEG-based emotion recognition in music listening. IEEE Transactions on Biomedical Engineering, 57(7):1798–1806.
McDaniel, B. T., Higgins, T. E., and Olsen, S. A. (2018). Facial expression recognition in educational technology: A systematic review. In Proceedings of the 18th International Conference on Educational Data Mining, pages 45–53.
Mendoza, F., Menezes, M., Sant’Anna, A., Ortiz Barrios, M., Samara, A., and Galway, L. (2019). Affective recognition from eeg signals: an integrated data-mining approach. Journal of Ambient Intelligence and Humanized Computing, 10.
Menezes, M. L. R., Samara, A., Galway, L., Sant’Anna, A., Verikas, A., Alonso-Fernandez, F., Wang, H., and Bond, R. (2017). Towards emotion recognition for virtual environments: An evaluation of EEG features on benchmark dataset. Personal and Ubiquitous Computing, 21(6):1003–1013.
Miranda-Correa, J., Abadi, M. K., Sebe, N., and Patras, I. (2021). Amigos: A dataset for affect, personality and mood research on individuals and groups. IEEE Transactions on Affective Computing, 12(2):479–493.
Murugappan, M., Ramachandran, N., and Sazali, Y. (2010). Classification of human emotion from EEG using discrete wavelet transform. Journal of Biomedical Science and Engineering, 3(4):390–396.
Picard, R. W. (1997). Affective Computing. MIT Press, Cambridge, MA, USA.
Rodríguez, A., Ocampo, M., and D’Mello, S. (2020). Improving engagement detection in e-learning contexts with advanced eeg features. In Proceedings of the 20th International Conference on Artificial Intelligence in Education (AIED), pages 321–331.
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6):1161–1178.
Sun, Y. and Li, C. (2022). Posture-based engagement detection for online learners. In Proceedings of the 22nd International Conference on Artificial Intelligence in Education (AIED), pages 210–221.
Woolf, B. P. and Burleson, W. (2019). Affect-aware learning technologies: From design to research. International Journal of Artificial Intelligence in Education, 29(3):371–381.
Zhang, Z., Wang, Y., Cai, J., and Hu, W. (2020). EEG-based Attention Monitoring and Task Difficulty Analysis in E-Learning. Computers & Education, 151:103832.
Zheng, W.-L., Zhu, J., and Lu, B.-L. (2015). Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks. IEEE Transactions on Autonomous Mental Development, 7(3):162–175.
Zhou, Y. and Kim, S. (2023). Exploring the relationships between student engagement, motivation, and performance in online learning environments. Humanities and Social Sciences Communications, 10(1):47.
Publicado
24/11/2025
Como Citar
MENEZES, M. Luiza R.; VICARI, Rosa Maria.
EduEmotion: An EEG Dataset of Emotional Responses in Simulated Learning Tasks. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 36. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 27-38.
DOI: https://doi.org/10.5753/sbie.2025.11689.
