Análisis de la reacción fisiológica cerebral del usuario de realidad virtual a través de la encefalografía (EEG)
Resumen
El electroencefalograma (EEG) es una herramienta muy útil para analizar las reacciones del cerebro a través del consumo de un contenido vinculado al uso de la tecnología de realidad virtual (RV). Nuestra propuesta consiste en una metodología basada en la neurociencia donde exploramos los efectos de la RV en la actividad cerebral de los usuarios. Esta metodología puede proporcionar un valioso método para comprender mejor el funcionamiento del cerebro y su relación con la percepción de estímulos provocados por el uso de RV. Al mismo tiempo, consideramos que la neurociencia puede inspirar y enriquecer el uso de la RV en la creación de nuevas formas artísticas, experiencias de entretenimiento y, en general, como medio de comunicación innovador explorando nuevas fronteras desconocidas hasta ahora por usuarios y audiencias. Estas investigaciones pueden tener también aplicación en campos como la psicología, la neurociencia, la psiquiatría, y los estudios de medios de comunicación y entretenimiento, además de suponer una valiosa herramienta para los creadores de contenidos, que de esta forma, obtienen información para descifrar los gustos del consumidor. De esta manera cada especialista en su disciplina será capaz de obtener datos que pueden aplicar de manera práctica para intervenir en sus respectivos campos de operación.
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Andreu-Sánchez, C., & Martín-Pascual, M. Á. (2021). Perception of cuts in different editing styles. Profesional de La Informacion, 30(2). https://doi.org/10.3145/EPI.2021.MAR.06
Seth, A. K., Suzuki, K., & Critchley, H. D. (2012). An Interoceptive Predictive Coding Model of Conscious Presence. Nombre de la Revista, Volumen(Número), https://doi.org/10.3389/fpsyg.2011.00395
Barrett, L. F., Mesquita, B., Ochsner, K. N., & Gross, J. J. (2007). The experience of emotion. Annual Review of Psychology, 58, 373–403. https://doi.org/10.1146/ANNUREV.PSYCH.58.110405.085709
Carbonell, F., Galán, L., Valdés, P., Worsley, K., Biscay, R. J., Díaz-Comas, L., Bobes, M. A., & Parra, M. (2004). Random Field–Union Intersection tests for EEG/MEG imaging. NeuroImage, 22(1), 268–276. https://doi.org/10.1016/J.NEUROIMAGE.2004.01.020
Cha, H. S., Chang, W. Du, Shin, Y. S., Jang, D. P., & Im, C. H. (2015). EEG-based neurocinematics: challenges and prospects. Brain-Computer Interfaces, 2(4), 186–192. https://doi.org/10.1080/2326263X.2015.1099091
Cheng, W., Wang, X., Zou, J., Li, M., & Tian, F. (2023). A High-Density EEG Study Investigating the Neural Correlates of Continuity Editing Theory in VR Films. Sensors, 23(13), 5886. https://doi.org/10.3390/s23135886
Christoforou, C., Christou-Champi, S., Constantinidou, F., & Theodorou, M. (2015). 14-From the eyes and the heart: A novel eye-gaze metric that predicts video preferences of a large audience. Frontiers in Psychology, 6(MAY). https://doi.org/10.3389/fpsyg.2015.00579
Diemer, J., Alpers, G. W., Peperkorn, H. M., Shiban, Y., & Mühlberger, A. (2015). The impact of perception and presence on emotional reactions: a review of research in virtual reality. Frontiers in Psychology, 6(JAN), 26–26. https://doi.org/10.3389/FPSYG.2015.00026
Dmochowski, J. P., Sajda, P., Dias, J., & Parra, L. C. (2012). Correlated components of ongoing EEG point to emotionally laden attention - a possible marker of engagement? Frontiers in Human Neuroscience, MAY 2012. https://doi.org/10.3389/FNHUM.2012.00112/FULL
Evans, A. C., Collins, D. L., Mills, S. R., Brown, E. D., Kelly, R. L., & Peters, T. M. (1993). 3D statistical neuroanatomical models from 305 MRI volumes. 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, pt 3, 1813–1817. https://doi.org/10.1109/NSSMIC.1993.373602
Freeman, D., Garety, P. A., Bebbington, P. E., Smith, B., Rollinson, R., Fowler, D., Kuipers, E., Ray, K., & Dunn, G. (2005). Psychological investigation of the structure of paranoia in a non-clinical population. The British Journal of Psychiatry, 186(5), 427–435. https://doi.org/10.1192/BJP.186.5.427
Gallese, V., & Guerra, M. (2022). The Neuroscience of Film (Journal). Projections (New York), 16(1), 1–10. https://doi.org/10.3167/PROJ.2022.160101
López, Á. G., Martínez, V. C., Alonso, T. O., Sánchez‐Pena, J. M., & Vergaz, R. (2022a). Emotion elicitation through vibrotactile stimulation as an alternative for deaf and hard of hearing people: an EEG study. Electronics, 11(14), 2196. https://doi.org/10.3390/ELECTRONICS11142196
Gautham Krishna, G., Krishna, G., & Bhalaji, N. (2017). 24-Electroencephalography Based Analysis of Emotions Among Indian Film Viewers. Communications in Computer and Information Science, 712, 145–155. https://doi.org/10.1007/978-981-10-5780-9_13
Geethanjali, B., Adalarasu, K., Hemapraba, A., Kumar, S. P., & Rajasekeran, R. (2017). Emotion analysis using SAM (Self-Assessment Manikin) scale. Biomedical Research-Tokyo.
Golnar-Nik, P., Farashi, S., & Safari, M. S. (2019). The application of EEG power for the prediction and interpretation of consumer decision-making: A neuromarketing study. Physiology and Behavior, 207, 90–98. https://doi.org/10.1016/j.physbeh.2019.04.025
Hasson, U., Landesman, O., Knappmeyer, B., Vallines, I., Rubin, N., & Heeger, D. J. (2008). 18b-Neurocinematics: The Neuroscience of Film. Projections, 2(1), 1–26. https://doi.org/10.3167/PROJ.2008.020102
He, L., Li, H., Xue, T., Sun, D., Zhu, S., & Ding, G. (2018). Am I in the theater? Usability Study of Live Performance Based Virtual Reality. 10. https://doi.org/10.1145/3281505.3281508
Hofmann, S. M., Klotzsche, F., Mariola, A., Nikulin, V. V., Villringer, A., & Gaebler, M. (2021). Decoding subjective emotional arousal from eeg during an immersive virtual reality experience. ELife, 10. https://doi.org/10.7554/ELIFE.64812
Ijsselsteijn, W., De Ridder, H., Freeman, J., Avons, S. E., & Bouwhuis, D. (2001). Effects of stereoscopic presentation, image motion, and screen size on subjective and objective corroborative measures of presence. Presence: Teleoperators and Virtual Environments, 10(3), 298–311. https://doi.org/10.1162/105474601300343621
Im, C.-H., Lee, J.-H., & Lim, J.-H. (2015). 16-Neurocinematics based on passive BCI: Decoding temporal change of emotional arousal during video watching from multi-channel EEG. 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015. https://doi.org/10.1109/ASCC.2015.7244792
Jalal, L., & Murroni, M. (2020). On the impact of single and multiple effects on quality of experience for multisensorial TV in smart home. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB, 2020-October. https://doi.org/10.1109/BMSB49480.2020.9379734
Jäncke, L. (2009). The plastic human brain. Restorative Neurology and Neuroscience, 27(5), 521–538. https://doi.org/10.3233/RNN-2009-0519
Khosravi Khorashad, S., & Khosrowabadi, R. (2022). 48-The Impact of the Hitchcockian Suspense Model and Its Associated Directing Style on the Horror Genre: A Neurocinematics Study. Quarterly Review of Film and Video. https://doi.org/10.1080/10509208.2022.2156251
Al Lang, P. J. (1985). The cognitive
psychophysiology of emotion: Fear and anxiety. In A. H. Tuma & J. D. Maser (Eds.), Anxiety and the anxiety disorders (pp. 131–170). Lawrence Erlbaum Associates, Inc. https://psycnet.apa.org/record/1985-97708-007
Lucia, M. J., Revuelta, P., García, Á., Ruiz, B., Vergaz, R., Cerdán, V., & Ortiz, T. (2020). Vibrotactile Captioning of Musical Effects in Audio-Visual Media as an Alternative for Deaf and Hard of Hearing People: An EEG Study. IEEE Access, 8, 190873–190881. https://doi.org/10.1109/ACCESS.2020.3032229
Marín‐Morales, J., Llinares, C., Guixeres, J., & Alcañíz, M. (2020). Emotion recognition in immersive virtual reality: From statistics to affective computing. Sensors, 20(18), 5163. https://doi.org/10.3390/s20185163
Sánchez, I. M., & Segura, J. (2018). Una perspectiva neurobiológica y comunicacional de la imagen y de la realidad aumentada. La Revista Icono 14, 16(1), 1-21. https://doi.org/10.7195/RI14.V16I1.1102
Ortiz Alonso, T., Matías Santos, J., Ortiz Terán, L., Borrego Hernández, M., Poch Broto, J., Alejandro de Erausquin, G., (2015). Differences in Early Stages of Tactile ERP Temporal Sequence (P100) in Cortical Organization during Passive Tactile Stimulation in Children with Blindness and Controls. PLoS ONE, 10(7), 124527. https://doi.org/10.1371/journal.pone.0124527
Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18(1), 49–65. https://doi.org/10.1016/0167-8760(84)90014-X
Slobounov, S. M., Ray, W., Johnson, B., Slobounov, E., & Newell, K. M. (2015). Modulation of cortical activity in 2D versus 3D virtual reality environments: An EEG study. International Journal of Psychophysiology, 95(3), 254-260. https://doi.org/10.1016/j.ijpsycho.2014.11.003
Smith, M. (2022). 65J-Triangulation Revisited. Projections, 16(1), 11–24. https://doi.org/10.3167/PROJ.2022.160102
Tian, F., Wang, X., Cheng, W., Lee, M., & Jin, Y. (2022). A Comparative Study on the Temporal Effects of 2D and VR Emotional Arousal. Sensors, 22(21), 8491–8491.
https://doi.org/10.3390/S22218491
Wang, Y., & Wang, Y. (2020). A Neurocinematic Study of the Suspense Effects in Hitchcock’s Psycho. Frontiers in Communication, 5. https://doi.org/10.3389/FCOMM.2020.576840
Zhao, G., Zhang, Y., Ge, Y., & Gasbarri, A. (2018). Frontal EEG Asymmetry and Middle Line Power Difference in Discrete Emotions. https://doi.org/10.3389/fnbeh.2018.00225
Derechos de autor 2023 Miguel Casas Arias & Victor Cerdán
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