343 research outputs found

    Field Output correction factors of small static field for IBA Razor NanoChamber

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    The goal of this work is to present results of field output factors (OF) using an IBA CC003 (Razor NanoChamber) and compared these results with PTW 60019 (MicroDiamond) and IBA Razor Diode. The experimental results for IBA CC003 were also compared with Monte Carlo (MC) Simulation, using Penelope and Ulysses programs. In addition, field output correction factors for IBA CC003 were derived with three different methods: 1) using PTW 60019 and IBA Razor as reference detectors; 2) comparison between MC and experimental measurements; and 3) using only MC. The beam collimation included in this study were 1) square field size between 10x10 and 0.5x0.5 cm2 defined by the MLC and jaws and 2) cones of different diameters. For IBA CC003 it was determined the polarity and ion collection efficiency correction factors in parallel and perpendicular orientation. The results indicate 1) the variation of polarity effect with the field size is relevant for the determination of OF using IBA CC003, especially for parallel orientation; 2) there is no significant variation of the ion collection efficiency with the field size using IBA CC003 in parallel orientation; 3) OF differences between IBA CC003 and PTW 60019/IBA Razor and experimental and MC results increase with decreasing field size. Results on the field output correction factor indicate 1) using the first and second method, the factor increase with decreasing field size, which can be related with the influence of the volume effect and 2) using the third method the factor decrease with decreasing field size, which can be explained by the perturbation effect.Comment: 23 pages, 10 figures, 11 table

    Dolo sin voluntad

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    It is understood that willful misconduct is, ab initio, knowledge, because only knowledge generates control and only this provides reasons to justify base the treatment of cases of willful behavior. Consequently, in blind situations, there is no place toSe entiende que el dolo es, ab initio, conocimiento, porque solo el conocimiento genera dominio y solo éste proporciona razones para fundamentar el tratamiento dispensado a los casos de actuación dolosa. En consecuencia, en la ceguera ante los hechos, n

    Legítima defensa de animales

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    If we recognize that animals have rights, even minimal and rudimentary as the right to not to be killed without an acceptable reason or the right to live without constant or repetitive pain (§ 17 Animal Protection Law), then the consequence is that they wSi reconocemos que los animales tienen derechos –así sean mínimos y rudimentarios como es de derecho a no ser matado sin una razón aceptable así como el derecho a vivir sin dolor constante o repetitivo (§ 17 Ley de Protección Animal)-  la consecuencia e

    EEG cortical activity and connectivity correlates of early sympathetic response during cold pressor test

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    Previous studies have identified several brain regions involved in the sympathetic response and its integration with pain, cognition, emotions and memory processes. However, little is known about how such regions dynamically interact during a sympathetic activation task. In this study, we analyzed EEG activity and effective connectivity during a cold pressor test (CPT). A source localization analysis identified a network of common active sources including the right precuneus (r-PCu), right and left precentral gyri (r-PCG, l-PCG), left premotor cortex (l-PMC) and left anterior cingulate cortex (l-ACC). We comprehensively analyzed the network dynamics by estimating power variation and causal interactions among the network regions through the direct directed transfer function (dDTF). A connectivity pattern dominated by interactions in α (8–12) Hz band was observed in the resting state, with r-PCu acting as the main hub of information flow. After the CPT onset, we observed an abrupt suppression of such α -band interactions, followed by a partial recovery towards the end of the task. On the other hand, an increase of δ -band (1–4) Hz interactions characterized the first part of CPT task. These results provide novel information on the brain dynamics induced by sympathetic stimuli. Our findings suggest that the observed suppression of α and rise of δ dynamical interactions could reflect non-pain-specific arousal and attention-related response linked to stimulus’ salience

    Is Hypnotic Induction Necessary to Experience Hypnosis and Responsible for Changes in Brain Activity?

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    The relevance of formal hypnotic induction to the experience of trance and its neural correlates is not clear, in that hypnotizability, beliefs and expectation of hypnosis may play a major role. The aim of the study was assessing the EEG brain activity of participants with high (highs) or low hypnotizability scores (lows), aware of their hypnotizability level and informed that the session will include simple relaxation, formal hypnotic induction and neutral hypnosis. A total of 16 highs and 15 lows (according to the Stanford Hypnotic Susceptibility Scale, form A) were enrolled. Their EEGs were recorded during consecutive conditions of open/closed-eyes relaxation, hypnotic induction, neutral hypnosis and post hypnosis not interrupted by interviews. The studied variables were theta, alpha and gamma power spectral density (PSD), and the Determinism (DET) and Entropy (ENT) of the EEG signal Multidimensional Recurrence Plot (mRP). Highs reported significantly greater changes in their state of consciousness than lows across the session. The theta, alpha and gamma PSD did not exhibit condition-related changes in both groups. The Alpha PSD was larger in highs than in lows on midline sites, and the different sides/regions’ theta and gamma PSD were observed in the two groups independently from conditions. ENT showed no correlation with hypnotizability, while DET positively correlated with hypnotizability during hypnosis. In conclusion, the relevance of formal hypnotic induction to the experience of trance may be scarce in highs, as they are aware of their hypnotizability scores and expecting hypnosis. Cognitive processing varies throughout the session depending on the hypnotizability level

    Proyecto de instalación de una planta embotelladora de agua de mesa y agua saborizada (Proyecto de Pre - Factibilidad)

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    El estudio de prefactibilidad realizado para el presente proyecto nos orienta y nos da información relevante que será de utilidad para la realización del estudio de factibilidad y la puesta en marcha. En el estudio de mercado se demuestra el consumo creciente del agua embotellada en la región Cusco, observándose el primer año una demanda insatisfecha de 1 591 546,6 litros, satisfaciendo el proyecto parte de dicha demanda en un volumen de 1 260 000 litros de agua embotellada, observándose similar comportamiento en los siguientes años. La planta de producción estará localizada en• la ciudad de Sicuani. En lo referente al tamaño de planta la capacidad el sistema de filtración será de 5 galones por minuto (1135.59 litros/hora), y la capacidad del sistema de embotellado es de 1500 botellas/hora para botellas de PET de 2 litros y 2000 botellas/hora para botellas de PET de 0,625 litros. La aplicación de la ósmosis inversa en el sistema de filtración, además de la automatización del sistema de embotellado, junto con los sistemas de aseguramiento de la calidad, garantizan la obtención de un producto que cumpla con la normatividad vigente para este tipo de productos. La inversión total del proyecto asciende al monto de S/. 985086,15 Nuevos Soles. El monto de la inversión para el financiamiento en un 30% será asumido por los socios y el restante 70% será financiado por crédito. Para el financiamiento del 70% de la inversión se plantea el uso de la línea de crédito del programa multisectorial de créditos para la pequeña empresa PROPEM BID de COFIDE, los recursos del Programa están constituidos por fondos del Banco Interamericano de Desarrollo - BID, EXIl\IIBANK del Japón y COFIDE. Por ser COFIDE un banco de segundo piso canaliza recursos financieros al mercado a través de otras instituciones financieras intermediarias (IFI). El monto financiado por COFIDE será de S/. 620604,27 Nuevos Soles y el monto restante de S/. 68956,03 Nuevos Soles por la IFI; los socios aportarán S/. 295525,85 Nuevos Soles. La organización de la empresa está regida por la ley N° 26887, tomando la forma de "Sociedad Comercial de Responsabilidad Limitada". La evaluación económica financiera muestra los siguientes resultados: Valor Actual Neto Económico de S/. 567 110,45 Nuevos Soles. Valor Actual Neto Financiero de S/. 611 710,52 Nuevos Soles. Tasa Interna de Retorno Económico de 29,60%. Tasa Interna de Retorno Financiero de 44,23%. Por lo que podemos manifestar que el proyecto es rentable económica y financieramente. En lo referente a la afectación del medio ambiente la planta de agua embotellada al no utilizar sustancias nocivas, ni generar gases tóxicos no presenta problemas de contaminación ambiental significativa.Tesi

    Finite size effects on pion spectra in relativistic heavy-ion collisions

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    We compute the pion inclusive transverse momentum distribution assuming thermal equilibrium together with transverse flow and accounting for finite size effects and energy loss at the time of decoupling. We compare to data on mid-rapidity pions produced in central collisions in RHIC at sqrt{s_{NN}}=200 GeV. We find that a finite size for the system of emitting particles results in a power-like fall-off of the spectra that follows the data up to larger p_t values, as compared to a simple thermal model.Comment: 6 figures, one new. References added. Expanded comments. Published versio

    Potential physiological stress biomarkers in human sweat

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    Emotional sweating occurs in response to affective stimuli like fear, anxiety, or stress and is more evident in specific parts of the body such as the palms, soles, and axillae. During emotional sweating, humans release many volatile organic compounds (VOCs) that could play a crucial role as possible com-municative signals of specific emotions. In this preliminary study, we investigated seven volatiles belonging to the chemical class of acids and released from the armpit as possible stress biomarkers. To this aim, we processed sweat VOCs and physiological stress correlates such as heart rate variability (HRV), electrodermal activity, and thermal imaging during a Stroop color-word test. Particularly, we modelled the variability of well-known stress markers extracted from the physiological signals as a function of the acid VOCs by means of LASSO regression. LASSO results revealed that the dodecanoic acid was the only selected regressor and it was able to significantly explain more than 64 % of the variance of both the mean temperature of the tip of the nose (p=0.018, R2=0.64) and of the mean HRV (p=0.011, R2=0.67). Although preliminary, our results suggest that dodecanoic acid could be a marker of the sympathetic nervous system response to stress stimuli, opening for the detection of new biomarkers of stress

    Taller de creatividad e innovación en medios digitales y festival de spots publicitarios de las asignaturas y los TFG vinculados al área de producción y realización publicitaria

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    El objetivo principal de este grupo de innovación es canalizar todas las actividades extra académicas que venimos realizando en torno a la realización publicitaria y hacerlas llegar al alumnado de la Universidad

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

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    [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.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.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.0223881S1241410Picard, R. W. (2003). Affective computing: challenges. International Journal of Human-Computer Studies, 59(1-2), 55-64. doi:10.1016/s1071-5819(03)00052-1Jerritta, 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.2011.5759912Harms, 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/s11065-010-9138-6Lindal, 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.2012.09.003Barrett, 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/nsx060Russell, 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-xCalvo, 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.2010.1Valenza, 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.2015.0176Valenza, 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.2011.30Valenza, 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.2013.2290382Marín-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/s41598-018-32063-4Nakisa, 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.2017.09.062Baños, 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.2004.7.734Lange, 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)00134-7Baños, 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_3Peperkorn, 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.22057Meehan, 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/s10484-005-6381-3Higuera-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.2017.05.006Bian, 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/s00779-016-0953-5Baños, 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.2012.04.002Riva, 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.2006.9993Vecchiato, 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/s10339-015-0725-6Slater, 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.1997.6.6.603Bishop, 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)00070-7Kort, 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:10.1162/105474603322391604Van 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.2015.00637Eberhard, J. P. (2009). Applying Neuroscience to Architecture. Neuron, 62(6), 753-756. doi:10.1016/j.neuron.2009.06.001Nanda, 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:10.1080/17508975.2013.807767Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161-1178. doi:10.1037/h0077714Slater, 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.1994.3.2.130Kroenke, 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.xBradley, 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)90063-9Cousineau, D., & Chartier, S. (2010). Outliers detection and treatment: a review. International Journal of Psychological Research, 3(1), 58-67. doi:10.21500/20112084.844Tarvainen, 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:10.1109/10.979357Tarvainen, 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.2013.07.024Rajendra 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/s11517-006-0119-0Richman, 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.h2039Peng, 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:10.1063/1.166141Grassberger, P., & Procaccia, I. (1983). Characterization of Strange Attractors. Physical Review Letters, 50(5), 346-349. doi:10.1103/physrevlett.50.346Delorme, 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.2003.10.009Colomer 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.2016.00074Kober, 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.2011.12.003Hyvärinen, A., & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), 411-430. doi:10.1016/s0893-6080(00)00026-5Mormann, 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)00087-7Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New Support Vector Algorithms. Neural Computation, 12(5), 1207-1245. doi:10.1162/089976600300015565Yan, 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.2015.02.025Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199Gorini, 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.2010.0100Glass, L. (2001). Synchronization and rhythmic processes in physiology. Nature, 410(6825), 277-284. doi:10.1038/35065745Stam, 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.2005.06.011Zhao, 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/s11434-008-0547-3Baumgartner, 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.2006.9.30Koelstra, 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.2011.15Kim, 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.2008.26Yuan-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.2010.2048568Combrisson, 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.2015.01.010De 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.2015.00576Mitchell, 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.2015.02.018Powers, 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.2007.04.006Critchley, 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.2009.01.012Niedenthal, P. M. (2007). Embodying Emotion. Science, 316(5827), 1002-1005. doi:10.1126/science.1136930Leer, 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.2014.04.004Gentili, 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.2017.2.4Debener, 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.
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