Análisis de la reacción fisiológica cerebral del usuario de realidad virtual a través de la encefalografía (EEG)

  1. Casas Arias, Miguel 1
  2. Cerdán Martínez, Victor 1
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Revista:
Comunicación & métodos

ISSN: 2659-9538

Año de publicación: 2023

Título del ejemplar: Transdisciplinarity in Communication Research Methodologies

Volumen: 5

Número: 2

Páginas: 19-32

Tipo: Artículo

DOI: 10.35951/V5I2.196 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Comunicación & métodos

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.

Referencias bibliográficas

  • 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