Reading the Mind at Play: EEG Insights into Player Engagement with Affective AI

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Ricardo Fernández-Rafael
Universidad de Deusto
https://orcid.org/0000-0002-7078-7561
Macarena Cuenca-Amigo
Universidad de Deusto
https://orcid.org/0000-0002-8629-7364
Alazne Mujika-Alberdi
Universidad de Deusto
https://orcid.org/0000-0001-9350-3384

Objectives. This study explores how Affective Artificial Intelligence (AI), systems capable of detecting and responding to human emotional states, impacts player engagement within a video game context. The objective is to assess whether emotional adaptivity significantly influences engagement levels and how individual traits such as gender and gaming experience moderate these effects.
Methodology. A controlled experimental design was employed using the psychological horror game Nevermind. Participants (n = 30) were randomly assigned to either an affective AI condition or a non-adaptive control group. EEG data were recorded throughout gameplay to obtain real-time engagement metrics. Participants were stratified by gender and gaming experience (novice, intermediate, advanced) to examine moderating effects. Statistical analyses included non-parametric comparisons and contingency tests
Findings. Results showed no significant overall difference in engagement between AI conditions. However, gender and gaming experience strongly influenced engagement. Female participants reported higher engagement regardless of condition. Advanced players demonstrated significantly higher engagement when exposed to Affective AI, suggesting that emotional adaptivity is particularly effective for experienced users. Novice and intermediate players maintained high baseline engagement levels independent of adaptation.
Value. This study contributes to affective computing and game design research by offering empirical evidence on how Affective AI influences engagement across user profiles. Using physiological data, it highlights the conditional effectiveness of emotional adaptivity and its potential as a tool for targeted engagement. These findings inform the development of more inclusive and responsive interactive experiences, especially for advanced players seeking novelty and challenge.

Keywords
Interactive Entertainment, Human-Computer Interaction, Emotional Responsiveness, Gaming Experience, Adaptive Game Design, EEG Metrics, Player Engagement, Affective Artificial Intelligence

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How to Cite
Fernández-Rafael, Ricardo et al. “Reading the Mind at Play: EEG Insights into Player Engagement with Affective AI”. Hipertext.net, 2025, no. 31, pp. 103-14, doi:10.31009/hipertext.net.2025.i31.10.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

(c) Ricardo Fernández-Rafael, Macarena Cuenca-Amigo, Alazne Mujika-Alberdi, 2025


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Author Biographies

Ricardo Fernández-Rafael, Universidad de Deusto

Ricardo Fernández Rafael holds a degree in Audiovisual Communication and an MBA from the EOI School of Industrial Organization. He is currently pursuing his PhD in Leisure, Culture, and Communication at the University of Deusto (Bilbao, Spain). Since becoming the CEO of Aluma3 Instore Services, he has brought his unique perspective and experience to the field of retail design and in-store marketing services. His passion for the video game industry is not only reflected in his professional work but also in his writing. As a regular contributor to various video game magazines, he has provided his insights and expertise on the gamer’s experience and the industry at large. His research work is being conducted in the context of his ongoing doctoral studies, which align with his interest in the broader implications of video game culture and its intersection with business, communication and leisure studies.

Macarena Cuenca-Amigo, Universidad de Deusto

Macarena Cuenca holds a PhD in Leisure & Human Development. She is associate professor at Deusto Business School, where she lectures Strategy and Business Organisation, and researcher at the Institute of Leisure Studies of the University of Deusto (Bilbao, Spain). Her main line of research is cultural audience development, the topic on which she prepared her doctoral thesis. She teaches at postgraduate level at various universities and has lead and taken part in several competitive European and domestic research projects. She has published papers in such scientific journals as Arts Management, Law, and Society; International Journal of Event and Festival Management and Museum Management and Curatorship. She is also committed to transferring knowledge into the cultural sector by participating in seminars, encounters, and workshops with cultural practitioners.

Alazne Mujika-Alberdi, Universidad de Deusto

Alazne Mujika-Alberdi holds a degree in Economics and Business Administration (1995) and a PhD in Economics and Business Administration (1999) from the University of Deusto. She also earned a Master’s Degree in Market Research and Information Systems from UNED (2008) and a University Expert Diploma in the Design and Statistical Analysis of Surveys for Market and Opinion Research (UNED, 2006). She is accredited as a professor by ANECA and UNIQUAL (Basque Quality Agency). Her research is conducted within the “Communication” team at the University of Deusto, which has received the highest recognition from the Basque Government for the periods 2019–2021 and 2022–2025. Her work focuses on the design and statistical analysis of opinion and market studies, especially in the field of “marketing and society.” She has authored over fifty publications, including journal articles, book chapters, and full-length books. She has also participated in more than 20 competitive and collaborative research projects.

References

Arslan, R. B. & Filiz, E. (2022). Enhancement of player experience in video games using EEG-based dynamic difficulty adjustment. In Proceedings of the 2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1–4). https://doi.org/10.1109/INISTA55318.2022.9894125

Bakkialakshmi, V. S. & Sudalaimuthu, T. (2022). Emo-Gem: An impacted affective emotional psychology analysis through Gaussian model using AMIGOS. Indian Journal of Computer Science and Engineering, 13(4), 1040–1055. https://doi.org/10.21817/indjcse/2022/v13i4/221304034

Beale, R. & Creed, C. (2009). Affective interaction: How emotional agents affect users. International Journal of Human-Computer Studies, 67(9), 755–776. https://doi.org/10.1016/j.ijhcs.2009.05.001

Bontchev, B. (2016). Adaptation in affective video games: A literature review. Cybernetics and Information Technologies, 16(3), 3–34. https://doi.org/10.1515/cait-2016-0032

Chanel, G., Rebetez, C., Bétrancourt, M. & Pun, T. (2011). Emotion assessment from physiological signals for adaptation of game difficulty. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 41(6), 1052–1063. https://doi.org/10.1109/TSMCA.2011.2116000

Csíkszentmihályi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.

Emotiv. (2015). Emotiv Insight [EEG headset and software]. Emotiv. https://www.emotiv.com/products/insight

Flying Mollusk. (2015). Nevermind [Video game]. Flying Mollusk.

Hartmann, T. & Klimmt, C. (2006). Gender and computer games: Exploring females’ dislikes. Journal of Computer-Mediated Communication, 11(4), 910–931. https://doi.org/10.1111/j.1083-6101.2006.00301.x

IBM Corp. (2023). IBM SPSS Statistics (Version 29) [Computer software]. IBM Corp. https://www.ibm.com/products/spss-statistics

Karpouzis, K. & Yannakakis, G. N. (Eds.). (2016). Emotion in games. Theory and Praxis. Springer International Publishing. https://doi.org/10.1007/978-3-319-41316-7

Melhart, D., Togelius, J., Mikkelsen, B., Holmgård, C. & Yannakakis, G. N. (2023). The ethics of AI in games. arXiv (arXiv:2305.07392). https://arxiv.org/abs/2305.07392

Ng, Y. Y., Khong, C. W. & Nathan, R. J. (2018). Evaluating affective user-centered design of video games using qualitative methods. International Journal of Computer Games Technology, 2018, article ID 3757083, 1–13. https://doi.org/10.1155/2018/3757083

Ninja Theory. (2017). Hellblade: Senua’s Sacrifice [Video game]. Ninja Theory.

Parsons, T. D., McMahan, T. & Parberry, I. (2022). Classification of video game player experience using consumer-grade electroencephalography. IEEE Transactions on Affective Computing, 13(1), 3–15. https://doi.org/10.1109/TAFFC.2020.2992437

Picard, R. W. (1997). Affective computing. MIT Press.

Ravichandran, K. & Ilango, S. K. (2023). Influence of AI-powered gaming developers and analyzing player behavior and enhancing user experience. In Proceedings of the 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI) (pp. 1–9). IEEE. https://doi.org/10.1109/ICAEECI58247.2023.10370952

Rukavina, S., Gruss, S., Hoffmann, H., Tan, J.-W., Walter, S. & Traue, H. C. (2016). Affective computing and the impact of gender and age. PLOS ONE, 11(3), e0150584. https://doi.org/10.1371/journal.pone.0150584

Smith, G. & Carette, J. (2020). Design foundations for emotional game characters. Eludamos: Journal for Computer Game Culture, 10(1), 109–140. https://doi.org/10.7557/23.6175

Wang, Y., Qian, K., Nelson, J., Yagi, H., Kishi, A., Morita, K. & Yamamoto, Y. (2020). Can affective computing better the mental status of the electronic games player? A perspective. In Proceedings of the 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech) (pp. 366–367). https://doi.org/10.1109/LifeTech48969.2020.1570620404

Yannakakis, G. N. & Paiva, A. (2015). Emotion in games. In R. Calvo, S. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing (pp. 459–471). Oxford University Press.

Zhang, S., Yan, Z., Sapkota, S., Zhao, S. & Ooi, W. T. (2021). Moment-to-moment continuous attention fluctuation monitoring through consumer-grade EEG device. Sensors, 21(10), 3419. https://doi.org/10.3390/s21103419