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Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors
Open accessAbstract
Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with non-immersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject’s arousal and valence perception. The model’s accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.
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The PHQ-9: Validity of a brief depression severity measure
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334
Welch, P. (1967) · IEEE Transactions on Audio and Electroacoustics · doi:10.1109/tau.1967.1161901 · Cited by 12,277
Measuring emotion: The self-assessment manikin and the semantic differential
Bradley, M. M., & Lang, P. J. (1994) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/0005-7916(94)90063-9 · Cited by 9,538
Independent component analysis: algorithms and applications
Hyvärinen, A., & Oja, E. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00026-5 · Cited by 8,705
Focus Groups: A Practical Guide for Applied Research
Billson, J. M. (1989) · Human Biology · Cited by 8,560
Physiological time-series analysis using approximate entropy and sample entropy
Richman, J. S., & Moorman, J. R. (2000) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2000.278.6.h2039 · Cited by 7,880
A Real-Time QRS Detection Algorithm
Pan, J., & Tompkins, W. J. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325532 · Cited by 7,855
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Dincelli, E., & Yayla, A. (2022) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2022.101717 · Cited by 490
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Górriz, J. M., Ramírez, J., Ortíz, A., Martínez-Murcia, F. J., Segovia, F., Suckling, J., Leming, M., Zhang, Y.-D., Álvarez-Sánchez, J. R., Bologna, G., et al. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.078 · Cited by 319
Vistorte, A. O. R., Deroncele-Acosta, A., Ayala, J. L. M., Barrasa, A., López-Granero, C., & Martí-González, M. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1387089 · Cited by 281
Metadata
- Authors
- Marín-Morales, J., Higuera-Trujillo, J. L., Greco, A., Guixeres, J., Llinares, C., Scilingo, E. P., Alcañiz, M., & Valenza, G.
- Venue
- Scientific Reports
- Published
- 2018-09-12
- Volume
- 8
- Issue
- 1
- Pages
- 13657-13657
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2890679134
- Cited by
- 411
- References
- 100
- FWCI
- 42.88
- Type
- journal article
- Language
- en
- Open access
- gold
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- Marín-Morales, J., Higuera-Trujillo, J. L., Greco, A., Guixeres, J., Llinares, C., Scilingo, E. P., Alcañiz, M., & Valenza, G. (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports. https://doi.org/10.1038/s41598-018-32063-4
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