- -

Real vs. immersive-virtual emotional experience: Analysis of psycho-physiological patterns in a free exploration of an art museum

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Real vs. immersive-virtual emotional experience: Analysis of psycho-physiological patterns in a free exploration of an art museum

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Marín-Morales, Javier es_ES
dc.contributor.author Higuera-Trujillo, Juan Luis es_ES
dc.contributor.author Greco, Alberto es_ES
dc.contributor.author Guixeres, Jaime es_ES
dc.contributor.author Llinares Millán, María Del Carmen es_ES
dc.contributor.author Gentili, Claudio es_ES
dc.contributor.author Scilingo, Enzo Pasquale es_ES
dc.contributor.author Alcañiz Raya, Mariano Luis es_ES
dc.contributor.author Valenza, Gaetano es_ES
dc.date.accessioned 2021-01-28T04:31:55Z
dc.date.available 2021-01-28T04:31:55Z
dc.date.issued 2019-10-15 es_ES
dc.identifier.issn 1932-6203 es_ES
dc.identifier.uri http://hdl.handle.net/10251/160083
dc.description.abstract [EN] Virtual reality is a powerful tool in human behaviour research. However, few studies compare its capacity to evoke the same emotional responses as in real scenarios. This study investigates psycho-physiological patterns evoked during the free exploration of an art museum and the museum virtualized through a 3D immersive virtual environment (IVE). An exploratory study involving 60 participants was performed, recording electroencephalographic and electrocardiographic signals using wearable devices. The real vs. virtual psychological comparison was performed using self-assessment emotional response tests, whereas the physiological comparison was performed through Support Vector Machine algorithms, endowed with an effective feature selection procedure for a set of state-of-the-art metrics quantifying cardiovascular and brain linear and nonlinear dynamics. We included an initial calibration phase, using standardized 2D and 360 degrees emotional stimuli, to increase the accuracy of the model. The self-assessments of the physical and virtual museum support the use of IVEs in emotion research. The 2-class (high/low) system accuracy was 71.52% and 77.08% along the arousal and valence dimension, respectively, in the physical museum, and 75.00% and 71.08% in the virtual museum. The previously presented 360 degrees stimuli contributed to increasing the accuracy in the virtual museum. Also, the real vs. virtual classifier accuracy was 95.27%, using only EEG mean phase coherency features, which demonstrates the high involvement of brain synchronization in emotional virtual reality processes. These findings provide an important contribution at a methodological level and to scientific knowledge, which will effectively guide future emotion elicitation and recognition systems using virtual reality. es_ES
dc.description.sponsorship This work was supported by Ministerio de Economia y Competitividad de Espana (URL: http://www.mineco.gob.es/; Project TIN201345736-R and DPI2016-77396-R); Direccion General de Trafico, Ministerio Del Interior de Espana (URL: http://www.dgt.es/es/; Project SPIP2017-02220); and the Institut Valencia d'Art Modern (URL: https://www.ivam.es/).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. es_ES
dc.language Inglés es_ES
dc.publisher Public Library of Science es_ES
dc.relation.ispartof PLoS ONE es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Affective computing es_ES
dc.subject Emotion recognition es_ES
dc.subject Eeg es_ES
dc.subject Ecg es_ES
dc.subject Support vector machine es_ES
dc.subject Virtual reality es_ES
dc.subject Head mounted display es_ES
dc.subject.classification CONSTRUCCIONES ARQUITECTONICAS es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Real vs. immersive-virtual emotional experience: Analysis of psycho-physiological patterns in a free exploration of an art museum es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1371/journal.pone.0223881 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2013-45736-R/ES/INVESTIGACION DE NUEVAS METRICAS DE NEUROARQUITECTURA MEDIANTE EL USO DE ENTORNOS VIRTUALES INMERSIVOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//BES-2014-069449/ES/BES-2014-069449/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2016-77396-R/ES/HERRAMIENTAS TERAPEUTICAS AVANZADAS PARA SALUD MENTAL/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/DGT//SPIP2017-02220/ES/Índice cognitivo-emocional de la percepción de seguridad del peatón/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Marín-Morales, J.; Higuera-Trujillo, JL.; Greco, A.; Guixeres, J.; Llinares Millán, MDC.; Gentili, C.; Scilingo, EP.... (2019). Real vs. immersive-virtual emotional experience: Analysis of psycho-physiological patterns in a free exploration of an art museum. PLoS ONE. 14(10):1-24. https://doi.org/10.1371/journal.pone.0223881 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1371/journal.pone.0223881 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 24 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 14 es_ES
dc.description.issue 10 es_ES
dc.identifier.pmid 31613927 es_ES
dc.identifier.pmcid PMC6793875 es_ES
dc.relation.pasarela S\397540 es_ES
dc.contributor.funder Ministerio del Interior es_ES
dc.contributor.funder Institut Valencià d'Art Modern es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Dirección General de Tráfico es_ES
dc.description.references Picard, R. W. (2003). Affective computing: challenges. International Journal of Human-Computer Studies, 59(1-2), 55-64. doi:10.1016/s1071-5819(03)00052-1 es_ES
dc.description.references Jerritta, S., Murugappan, M., Nagarajan, R., & Wan, K. (2011). Physiological signals based human emotion Recognition: a review. 2011 IEEE 7th International Colloquium on Signal Processing and its Applications. doi:10.1109/cspa.2011.5759912 es_ES
dc.description.references Harms, M. B., Martin, A., & Wallace, G. L. (2010). Facial Emotion Recognition in Autism Spectrum Disorders: A Review of Behavioral and Neuroimaging Studies. Neuropsychology Review, 20(3), 290-322. doi:10.1007/s11065-010-9138-6 es_ES
dc.description.references Lindal, P. J., & Hartig, T. (2013). Architectural variation, building height, and the restorative quality of urban residential streetscapes. Journal of Environmental Psychology, 33, 26-36. doi:10.1016/j.jenvp.2012.09.003 es_ES
dc.description.references Barrett, L. F. (2017). The theory of constructed emotion: an active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience, 12(11), 1833-1833. doi:10.1093/scan/nsx060 es_ES
dc.description.references Russell, J. A., & Mehrabian, A. (1977). Evidence for a three-factor theory of emotions. Journal of Research in Personality, 11(3), 273-294. doi:10.1016/0092-6566(77)90037-x es_ES
dc.description.references Calvo, R. A., & D’Mello, S. (2010). Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications. IEEE Transactions on Affective Computing, 1(1), 18-37. doi:10.1109/t-affc.2010.1 es_ES
dc.description.references Valenza, G., Greco, A., Gentili, C., Lanata, A., Sebastiani, L., Menicucci, D., … Scilingo, E. P. (2016). Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2067), 20150176. doi:10.1098/rsta.2015.0176 es_ES
dc.description.references Valenza, G., Lanata, A., & Scilingo, E. P. (2012). The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition. IEEE Transactions on Affective Computing, 3(2), 237-249. doi:10.1109/t-affc.2011.30 es_ES
dc.description.references Valenza, G., Nardelli, M., Lanata, A., Gentili, C., Bertschy, G., Paradiso, R., & Scilingo, E. P. (2014). Wearable Monitoring for Mood Recognition in Bipolar Disorder Based on History-Dependent Long-Term Heart Rate Variability Analysis. IEEE Journal of Biomedical and Health Informatics, 18(5), 1625-1635. doi:10.1109/jbhi.2013.2290382 es_ES
dc.description.references Marín-Morales, J., Higuera-Trujillo, J. L., Greco, A., Guixeres, J., Llinares, C., Scilingo, E. P., … Valenza, G. (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports, 8(1). doi:10.1038/s41598-018-32063-4 es_ES
dc.description.references Nakisa, B., Rastgoo, M. N., Tjondronegoro, D., & Chandran, V. (2018). Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Systems with Applications, 93, 143-155. doi:10.1016/j.eswa.2017.09.062 es_ES
dc.description.references Baños, R. M., Botella, C., Alcañiz, M., Liaño, V., Guerrero, B., & Rey, B. (2004). Immersion and Emotion: Their Impact on the Sense of Presence. CyberPsychology & Behavior, 7(6), 734-741. doi:10.1089/cpb.2004.7.734 es_ES
dc.description.references Lange, E. (2001). The limits of realism: perceptions of virtual landscapes. Landscape and Urban Planning, 54(1-4), 163-182. doi:10.1016/s0169-2046(01)00134-7 es_ES
dc.description.references Baños, R. M., Liaño, V., Botella, C., Alcañiz, M., Guerrero, B., & Rey B. Changing induced moods via virtual reality. In: Springer, Berlin H, editor. International Conference on Persuasive Technology. 2006. pp. 7–15. doi: 10.1007/11755494_3 es_ES
dc.description.references Peperkorn, H. M., Alpers, G. W., & Mühlberger, A. (2013). Triggers of Fear: Perceptual Cues Versus Conceptual Information in Spider Phobia. Journal of Clinical Psychology, 70(7), 704-714. doi:10.1002/jclp.22057 es_ES
dc.description.references Meehan, M., Razzaque, S., Insko, B., Whitton, M., & Brooks, F. P. (2005). Review of Four Studies on the Use of Physiological Reaction as a Measure of Presence in StressfulVirtual Environments. Applied Psychophysiology and Biofeedback, 30(3), 239-258. doi:10.1007/s10484-005-6381-3 es_ES
dc.description.references Higuera-Trujillo, J. L., López-Tarruella Maldonado, J., & Llinares Millán, C. (2017). Psychological and physiological human responses to simulated and real environments: A comparison between Photographs, 360° Panoramas, and Virtual Reality. Applied Ergonomics, 65, 398-409. doi:10.1016/j.apergo.2017.05.006 es_ES
dc.description.references Bian, Y., Yang, C., Gao, F., Li, H., Zhou, S., Li, H., … Meng, X. (2016). A framework for physiological indicators of flow in VR games: construction and preliminary evaluation. Personal and Ubiquitous Computing, 20(5), 821-832. doi:10.1007/s00779-016-0953-5 es_ES
dc.description.references Baños, R. M., Etchemendy, E., Castilla, D., García-Palacios, A., Quero, S., & Botella, C. (2012). Positive mood induction procedures for virtual environments designed for elderly people. Interacting with Computers, 24(3), 131-138. doi:10.1016/j.intcom.2012.04.002 es_ES
dc.description.references Riva, G., Mantovani, F., Capideville, C. S., Preziosa, A., Morganti, F., Villani, D., … Alcañiz, M. (2007). Affective Interactions Using Virtual Reality: The Link between Presence and Emotions. CyberPsychology & Behavior, 10(1), 45-56. doi:10.1089/cpb.2006.9993 es_ES
dc.description.references Vecchiato, G., Jelic, A., Tieri, G., Maglione, A. G., De Matteis, F., & Babiloni, F. (2015). Neurophysiological correlates of embodiment and motivational factors during the perception of virtual architectural environments. Cognitive Processing, 16(S1), 425-429. doi:10.1007/s10339-015-0725-6 es_ES
dc.description.references Slater, M., & Wilbur, S. (1997). A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments. Presence: Teleoperators and Virtual Environments, 6(6), 603-616. doi:10.1162/pres.1997.6.6.603 es_ES
dc.description.references Bishop, I. ., & Rohrmann, B. (2003). Subjective responses to simulated and real environments: a comparison. Landscape and Urban Planning, 65(4), 261-277. doi:10.1016/s0169-2046(03)00070-7 es_ES
dc.description.references Kort, Y. A. W. de, IJsselsteijn, W. A., Kooijman, J., & Schuurmans, Y. (2003). Virtual Laboratories: Comparability of Real and Virtual Environments for Environmental Psychology. Presence: Teleoperators and Virtual Environments, 12(4), 360-373. doi:10.1162/105474603322391604 es_ES
dc.description.references Van der Ham, I. J. M., Faber, A. M. E., Venselaar, M., van Kreveld, M. J., & Löffler, M. (2015). Ecological validity of virtual environments to assess human navigation ability. Frontiers in Psychology, 6. doi:10.3389/fpsyg.2015.00637 es_ES
dc.description.references Eberhard, J. P. (2009). Applying Neuroscience to Architecture. Neuron, 62(6), 753-756. doi:10.1016/j.neuron.2009.06.001 es_ES
dc.description.references Nanda, U., Pati, D., Ghamari, H., & Bajema, R. (2013). Lessons from neuroscience: form follows function, emotions follow form. Intelligent Buildings International, 5(sup1), 61-78. doi:10.1080/17508975.2013.807767 es_ES
dc.description.references Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161-1178. doi:10.1037/h0077714 es_ES
dc.description.references Slater, M., Usoh, M., & Steed, A. (1994). Depth of Presence in Virtual Environments. Presence: Teleoperators and Virtual Environments, 3(2), 130-144. doi:10.1162/pres.1994.3.2.130 es_ES
dc.description.references Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9. Journal of General Internal Medicine, 16(9), 606-613. doi:10.1046/j.1525-1497.2001.016009606.x es_ES
dc.description.references Bradley, M. M., & 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. doi:10.1016/0005-7916(94)90063-9 es_ES
dc.description.references Cousineau, D., & Chartier, S. (2010). Outliers detection and treatment: a review. International Journal of Psychological Research, 3(1), 58-67. doi:10.21500/20112084.844 es_ES
dc.description.references Tarvainen, M. P., Ranta-aho, P. O., & Karjalainen, P. A. (2002). An advanced detrending method with application to HRV analysis. IEEE Transactions on Biomedical Engineering, 49(2), 172-175. doi:10.1109/10.979357 es_ES
dc.description.references Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-aho, P. O., & Karjalainen, P. A. (2014). Kubios HRV – Heart rate variability analysis software. Computer Methods and Programs in Biomedicine, 113(1), 210-220. doi:10.1016/j.cmpb.2013.07.024 es_ES
dc.description.references Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., & Suri, J. S. (2006). Heart rate variability: a review. Medical & Biological Engineering & Computing, 44(12), 1031-1051. doi:10.1007/s11517-006-0119-0 es_ES
dc.description.references Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039 es_ES
dc.description.references Peng, C. ‐K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5(1), 82-87. doi:10.1063/1.166141 es_ES
dc.description.references Grassberger, P., & Procaccia, I. (1983). Characterization of Strange Attractors. Physical Review Letters, 50(5), 346-349. doi:10.1103/physrevlett.50.346 es_ES
dc.description.references Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. doi:10.1016/j.jneumeth.2003.10.009 es_ES
dc.description.references Colomer Granero, A., Fuentes-Hurtado, F., Naranjo Ornedo, V., Guixeres Provinciale, J., Ausín, J. M., & Alcañiz Raya, M. (2016). A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Frontiers in Computational Neuroscience, 10. doi:10.3389/fncom.2016.00074 es_ES
dc.description.references Kober, S. E., Kurzmann, J., & Neuper, C. (2012). Cortical correlate of spatial presence in 2D and 3D interactive virtual reality: An EEG study. International Journal of Psychophysiology, 83(3), 365-374. doi:10.1016/j.ijpsycho.2011.12.003 es_ES
dc.description.references Hyvärinen, A., & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), 411-430. doi:10.1016/s0893-6080(00)00026-5 es_ES
dc.description.references Mormann, F., Lehnertz, K., David, P., & E. Elger, C. (2000). Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D: Nonlinear Phenomena, 144(3-4), 358-369. doi:10.1016/s0167-2789(00)00087-7 es_ES
dc.description.references Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New Support Vector Algorithms. Neural Computation, 12(5), 1207-1245. doi:10.1162/089976600300015565 es_ES
dc.description.references Yan, K., & Zhang, D. (2015). Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212, 353-363. doi:10.1016/j.snb.2015.02.025 es_ES
dc.description.references Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199 es_ES
dc.description.references Gorini, A., Capideville, C. S., De Leo, G., Mantovani, F., & Riva, G. (2011). The Role of Immersion and Narrative in Mediated Presence: The Virtual Hospital Experience. Cyberpsychology, Behavior, and Social Networking, 14(3), 99-105. doi:10.1089/cyber.2010.0100 es_ES
dc.description.references Glass, L. (2001). Synchronization and rhythmic processes in physiology. Nature, 410(6825), 277-284. doi:10.1038/35065745 es_ES
dc.description.references Stam, C. J. (2005). Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clinical Neurophysiology, 116(10), 2266-2301. doi:10.1016/j.clinph.2005.06.011 es_ES
dc.description.references Zhao, Q., Zhang, L., & Cichocki, A. (2009). EEG-based asynchronous BCI control of a car in 3D virtual reality environments. Chinese Science Bulletin, 54(1), 78-87. doi:10.1007/s11434-008-0547-3 es_ES
dc.description.references Baumgartner, T., Valko, L., Esslen, M., & Jäncke, L. (2006). Neural Correlate of Spatial Presence in an Arousing and Noninteractive Virtual Reality: An EEG and Psychophysiology Study. CyberPsychology & Behavior, 9(1), 30-45. doi:10.1089/cpb.2006.9.30 es_ES
dc.description.references Koelstra, S., Muhl, C., Soleymani, M., Jong-Seok Lee, Yazdani, A., Ebrahimi, T., … Patras, I. (2012). DEAP: A Database for Emotion Analysis ;Using Physiological Signals. IEEE Transactions on Affective Computing, 3(1), 18-31. doi:10.1109/t-affc.2011.15 es_ES
dc.description.references Kim, J., & Andre, E. (2008). Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 2067-2083. doi:10.1109/tpami.2008.26 es_ES
dc.description.references Yuan-Pin Lin, Chi-Hong Wang, Tzyy-Ping Jung, Tien-Lin Wu, Shyh-Kang Jeng, Jeng-Ren Duann, & Jyh-Horng Chen. (2010). EEG-Based Emotion Recognition in Music Listening. IEEE Transactions on Biomedical Engineering, 57(7), 1798-1806. doi:10.1109/tbme.2010.2048568 es_ES
dc.description.references Combrisson, E., & Jerbi, K. (2015). Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. Journal of Neuroscience Methods, 250, 126-136. doi:10.1016/j.jneumeth.2015.01.010 es_ES
dc.description.references De Borst, A. W., & de Gelder, B. (2015). Is it the real deal? Perception of virtual characters versus humans: an affective cognitive neuroscience perspective. Frontiers in Psychology, 6. doi:10.3389/fpsyg.2015.00576 es_ES
dc.description.references Mitchell, R. L. C., & Phillips, L. H. (2015). The overlapping relationship between emotion perception and theory of mind. Neuropsychologia, 70, 1-10. doi:10.1016/j.neuropsychologia.2015.02.018 es_ES
dc.description.references Powers, M. B., & Emmelkamp, P. M. G. (2008). Virtual reality exposure therapy for anxiety disorders: A meta-analysis. Journal of Anxiety Disorders, 22(3), 561-569. doi:10.1016/j.janxdis.2007.04.006 es_ES
dc.description.references Critchley, H. D. (2009). Psychophysiology of neural, cognitive and affective integration: fMRI and autonomic indicants. International Journal of Psychophysiology, 73(2), 88-94. doi:10.1016/j.ijpsycho.2009.01.012 es_ES
dc.description.references Niedenthal, P. M. (2007). Embodying Emotion. Science, 316(5827), 1002-1005. doi:10.1126/science.1136930 es_ES
dc.description.references Leer, A., Engelhard, I. M., & van den Hout, M. A. (2014). How eye movements in EMDR work: Changes in memory vividness and emotionality. Journal of Behavior Therapy and Experimental Psychiatry, 45(3), 396-401. doi:10.1016/j.jbtep.2014.04.004 es_ES
dc.description.references Gentili, C. (2017). Why do we keep failing in identifying reliable biological markers in depression? Journal of Evidence-Based Psychotherapies, 17(2), 59-84. doi:10.24193/jebp.2017.2.4 es_ES
dc.description.references Debener, S., Minow, F., Emkes, R., Gandras, K., & de Vos, M. (2012). How about taking a low-cost, small, and wireless EEG for a walk? Psychophysiology, 49(11), 1617-1621. doi:10.1111/j.1469-8986.2012.01471.x es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem