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
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
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
[+]
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Eberhard, J. P. (2009). Applying Neuroscience to Architecture. Neuron, 62(6), 753-756. doi:10.1016/j.neuron.2009.06.001
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
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161-1178. doi:10.1037/h0077714
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
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
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
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
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
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
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
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
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
Grassberger, P., & Procaccia, I. (1983). Characterization of Strange Attractors. Physical Review Letters, 50(5), 346-349. doi:10.1103/physrevlett.50.346
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
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
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
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
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
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
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
Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199
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
Glass, L. (2001). Synchronization and rhythmic processes in physiology. Nature, 410(6825), 277-284. doi:10.1038/35065745
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
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
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
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
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
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
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
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
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
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
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
Niedenthal, P. M. (2007). Embodying Emotion. Science, 316(5827), 1002-1005. doi:10.1126/science.1136930
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
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
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
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