Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Joudeh, I. O.; Cretu, A. -M.; Bouchard, S.
Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features † Journal Article
In: Sensors, vol. 24, no. 13, 2024, ISSN: 14248220 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Abstract | Links | BibTeX | Tags: adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features
@article{joudeh_predicting_2024,
title = {Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features †},
author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198382238&doi=10.3390%2fs24134398&partnerID=40&md5=cefa8b2e2c044d02f99662af350007db},
doi = {10.3390/s24134398},
issn = {14248220 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Sensors},
volume = {24},
number = {13},
abstract = {The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features},
pubstate = {published},
tppubtype = {article}
}
The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.
2.
Joudeh, I. O.; Cretu, A. -M.; Bouchard, S.; Guimond, S.
Prediction of Continuous Emotional Measures through Physiological and Visual Data † Journal Article
In: Sensors, vol. 23, no. 12, 2023, ISSN: 14248220, (Publisher: MDPI).
Abstract | Links | BibTeX | Tags: Affect recognition, Affective state, Arousal, Data-source, Deep learning, Electrocardiography, emotion, Emotion Recognition, Emotions, face recognition, Faces detection, Forecasting, human, Humans, Images processing, Learning systems, Machine learning, Machine-learning, mental disease, Mental Disorders, Physiological data, physiology, Signal-processing, Statistical tests, Video recording, Virtual-reality environment
@article{joudeh_prediction_2023,
title = {Prediction of Continuous Emotional Measures through Physiological and Visual Data †},
author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard and S. Guimond},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163943735&doi=10.3390%2fs23125613&partnerID=40&md5=5e970f0d8c5790b85d8d77a9f3f52a2d},
doi = {10.3390/s23125613},
issn = {14248220},
year = {2023},
date = {2023-01-01},
journal = {Sensors},
volume = {23},
number = {12},
abstract = {The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments. © 2023 by the authors.},
note = {Publisher: MDPI},
keywords = {Affect recognition, Affective state, Arousal, Data-source, Deep learning, Electrocardiography, emotion, Emotion Recognition, Emotions, face recognition, Faces detection, Forecasting, human, Humans, Images processing, Learning systems, Machine learning, Machine-learning, mental disease, Mental Disorders, Physiological data, physiology, Signal-processing, Statistical tests, Video recording, Virtual-reality environment},
pubstate = {published},
tppubtype = {article}
}
The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments. © 2023 by the authors.
3.
Joudeh, I. O.; Cretu, A. -M.; Bouchard, S.; Guimond, S.
Prediction of Emotional States from Partial Facial Features for Virtual Reality Applications Journal Article
In: Annual Review of CyberTherapy and Telemedicine, vol. 21, pp. 17–21, 2023, ISSN: 15548716, (Publisher: Interactive Media Institute).
Abstract | Links | BibTeX | Tags: Arousal, article, clinical article, convolutional neural network, correlation coefficient, data base, emotion, facies, female, human, human experiment, Image processing, long short term memory network, male, random forest, residual neural network, root mean squared error, videorecording, virtual reality
@article{joudeh_prediction_2023-1,
title = {Prediction of Emotional States from Partial Facial Features for Virtual Reality Applications},
author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard and S. Guimond},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182471413&partnerID=40&md5=8190e0dbb5b48ae508515f4029b0a0d1},
issn = {15548716},
year = {2023},
date = {2023-01-01},
journal = {Annual Review of CyberTherapy and Telemedicine},
volume = {21},
pages = {17–21},
abstract = {The availability of virtual reality (VR) in numerous clinical contexts has been made possible by recent technological advancements. One application is using VR for cognitive interventions with individuals who have mental disorders. Predicting the emotional states of users could help to prevent their discouragement during VR interventions. We can monitor the emotional states of individuals using sensors like an external camera, as they engage in various tasks within VR environments. The emotional state of VR users can be measured through arousal and valence, as per the Circumplex model. We used the Remote Collaborative and Affective Interactions (RECOLA) database of emotional behaviours. We processed video frames from 18 RECOLA videos. Due to the headset in VR systems, we detected faces and cropped the images of faces to use the lower half of the face only. We labeled the images with arousal and valence values to reflect various emotions. Convolutional neural networks (CNNs), specifically MobileNet-v2 and ResNets-18, were then used to predict arousal and valence values. MobileNet-v2 outperforms ResNet-18 as well as others from the literature. We achieved a root mean squared error (RMSE), Pearson’s correlation coefficient (PCC), and Concordance correlation coefficient (CCC) of 0.1495, 0.6387, and 0.6081 for arousal, and 0.0996, 0.6453, and 0.6232 for valence. Our work acts as a proof-of-concept for predicting emotional states from arousal and valence values via visual data of users immersed in VR experiences. In the future, predicted emotions could be used to automatically adjust the VR environment for individuals engaged in cognitive interventions. © 2023, Interactive Media Institute. All rights reserved.},
note = {Publisher: Interactive Media Institute},
keywords = {Arousal, article, clinical article, convolutional neural network, correlation coefficient, data base, emotion, facies, female, human, human experiment, Image processing, long short term memory network, male, random forest, residual neural network, root mean squared error, videorecording, virtual reality},
pubstate = {published},
tppubtype = {article}
}
The availability of virtual reality (VR) in numerous clinical contexts has been made possible by recent technological advancements. One application is using VR for cognitive interventions with individuals who have mental disorders. Predicting the emotional states of users could help to prevent their discouragement during VR interventions. We can monitor the emotional states of individuals using sensors like an external camera, as they engage in various tasks within VR environments. The emotional state of VR users can be measured through arousal and valence, as per the Circumplex model. We used the Remote Collaborative and Affective Interactions (RECOLA) database of emotional behaviours. We processed video frames from 18 RECOLA videos. Due to the headset in VR systems, we detected faces and cropped the images of faces to use the lower half of the face only. We labeled the images with arousal and valence values to reflect various emotions. Convolutional neural networks (CNNs), specifically MobileNet-v2 and ResNets-18, were then used to predict arousal and valence values. MobileNet-v2 outperforms ResNet-18 as well as others from the literature. We achieved a root mean squared error (RMSE), Pearson’s correlation coefficient (PCC), and Concordance correlation coefficient (CCC) of 0.1495, 0.6387, and 0.6081 for arousal, and 0.0996, 0.6453, and 0.6232 for valence. Our work acts as a proof-of-concept for predicting emotional states from arousal and valence values via visual data of users immersed in VR experiences. In the future, predicted emotions could be used to automatically adjust the VR environment for individuals engaged in cognitive interventions. © 2023, Interactive Media Institute. All rights reserved.
4.
Tremblay, L.; Bouchard, S.; Chebbi, B.; Wei, L.; Monthuy-Blanc, J.; Boulanger, D.
The development of a haptic virtual reality environment to study body image and affect Journal Article
In: Annual Review of CyberTherapy and Telemedicine, vol. 11, pp. 80–84, 2013, ISSN: 15548716, (Publisher: Virtual reality med institute).
Abstract | Links | BibTeX | Tags: Affect, Arousal, art, article, Biofeedback, body image, computer interface, Emotional communications, female, happiness, Haptic devices, Haptics, human, Human bodies, human experiment, Humans, male, methodology, Mood, motor performance, physiology, psychological aspect, psychology, Psychomotor Performance, Psychophysiology, sadness, Touch, User-Computer Interface, velocity, virtual reality, Virtual-reality environment, Young Adult
@article{tremblay_development_2013,
title = {The development of a haptic virtual reality environment to study body image and affect},
author = {L. Tremblay and S. Bouchard and B. Chebbi and L. Wei and J. Monthuy-Blanc and D. Boulanger},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894231106&partnerID=40&md5=79731f3a31e9e70fcf3bf8f5db1f7d7c},
issn = {15548716},
year = {2013},
date = {2013-01-01},
journal = {Annual Review of CyberTherapy and Telemedicine},
volume = {11},
pages = {80–84},
abstract = {We report the results of a preliminary study testing the effect of participants’ mood rating on visual motor performance using a haptic device to manipulate a cartoonish human body. Our results suggest that moods involving high arousal (e.g. happiness) produce larger movements whereas mood involving low arousal (e.g. sadness) produce slower speed of performance. Our results are used for the development of a new haptic virtual reality application that we briefly present here. This application is intended to create a more interactive and motivational environment to treat body image issues and for emotional communication. © 2013 Interactive Media Institute.},
note = {Publisher: Virtual reality med institute},
keywords = {Affect, Arousal, art, article, Biofeedback, body image, computer interface, Emotional communications, female, happiness, Haptic devices, Haptics, human, Human bodies, human experiment, Humans, male, methodology, Mood, motor performance, physiology, psychological aspect, psychology, Psychomotor Performance, Psychophysiology, sadness, Touch, User-Computer Interface, velocity, virtual reality, Virtual-reality environment, Young Adult},
pubstate = {published},
tppubtype = {article}
}
We report the results of a preliminary study testing the effect of participants’ mood rating on visual motor performance using a haptic device to manipulate a cartoonish human body. Our results suggest that moods involving high arousal (e.g. happiness) produce larger movements whereas mood involving low arousal (e.g. sadness) produce slower speed of performance. Our results are used for the development of a new haptic virtual reality application that we briefly present here. This application is intended to create a more interactive and motivational environment to treat body image issues and for emotional communication. © 2013 Interactive Media Institute.
5.
Bouchard, S.; Bernier, F.; Boivin, E.; Morin, B.; Robillard, G.
Using biofeedback while immersed in a stressful videogame increases the effectiveness of stress management skills in soldiers Journal Article
In: PLoS ONE, vol. 7, no. 4, 2012, ISSN: 19326203.
Abstract | Links | BibTeX | Tags: adult, Arousal, article, Biofeedback, clinical trial, controlled clinical trial, controlled study, explosion, feedback system, first aid, Heart Rate, human, human experiment, Humans, Hydrocortisone, male, mental stress, metabolism, methodology, Military Personnel, military phenomena, normal human, pathophysiology, Psychological, psychological aspect, psychology, Psychophysiology, randomized controlled trial, recreation, Saliva, saliva level, simulation, soldier, Stress, Stress management, Video Games
@article{bouchard_using_2012,
title = {Using biofeedback while immersed in a stressful videogame increases the effectiveness of stress management skills in soldiers},
author = {S. Bouchard and F. Bernier and E. Boivin and B. Morin and G. Robillard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84860485827&doi=10.1371%2fjournal.pone.0036169&partnerID=40&md5=4b29a984169d2fe12d54f4c562344723},
doi = {10.1371/journal.pone.0036169},
issn = {19326203},
year = {2012},
date = {2012-01-01},
journal = {PLoS ONE},
volume = {7},
number = {4},
abstract = {This study assessed the efficacy of using visual and auditory biofeedback while immersed in a tridimensional videogame to practice a stress management skill (tactical breathing). All 41 participants were soldiers who had previously received basic stress management training and first aid training in combat. On the first day, they received a 15-minute refresher briefing and were randomly assigned to either: (a) no additional stress management training (SMT) for three days, or (b) 30-minute sessions (one per day for three days) of biofeedback-assisted SMT while immersed in a horror/first-person shooter game. The training was performed in a dark and enclosed environment using a 50-inch television with active stereoscopic display and loudspeakers. On the last day, all participants underwent a live simulated ambush with an improvised explosive device, where they had to provide first aid to a wounded soldier. Stress levels were measured with salivary cortisol collected when waking-up, before and after the live simulation. Stress was also measured with heart rate at baseline, during an apprehension phase, and during the live simulation. Repeated-measure ANOVAs and ANCOVAs confirmed that practicing SMT was effective in reducing stress. Results are discussed in terms of the advantages of the proposed program for military personnel and the need to practice SMT. © 2012 Bouchard et al.},
keywords = {adult, Arousal, article, Biofeedback, clinical trial, controlled clinical trial, controlled study, explosion, feedback system, first aid, Heart Rate, human, human experiment, Humans, Hydrocortisone, male, mental stress, metabolism, methodology, Military Personnel, military phenomena, normal human, pathophysiology, Psychological, psychological aspect, psychology, Psychophysiology, randomized controlled trial, recreation, Saliva, saliva level, simulation, soldier, Stress, Stress management, Video Games},
pubstate = {published},
tppubtype = {article}
}
This study assessed the efficacy of using visual and auditory biofeedback while immersed in a tridimensional videogame to practice a stress management skill (tactical breathing). All 41 participants were soldiers who had previously received basic stress management training and first aid training in combat. On the first day, they received a 15-minute refresher briefing and were randomly assigned to either: (a) no additional stress management training (SMT) for three days, or (b) 30-minute sessions (one per day for three days) of biofeedback-assisted SMT while immersed in a horror/first-person shooter game. The training was performed in a dark and enclosed environment using a 50-inch television with active stereoscopic display and loudspeakers. On the last day, all participants underwent a live simulated ambush with an improvised explosive device, where they had to provide first aid to a wounded soldier. Stress levels were measured with salivary cortisol collected when waking-up, before and after the live simulation. Stress was also measured with heart rate at baseline, during an apprehension phase, and during the live simulation. Repeated-measure ANOVAs and ANCOVAs confirmed that practicing SMT was effective in reducing stress. Results are discussed in terms of the advantages of the proposed program for military personnel and the need to practice SMT. © 2012 Bouchard et al.
6.
Renaud, P.; Goyette, M.; Chartier, S.; Zhornitski, S.; Trottier, D.; Rouleau, J. -L.; Proulx, J.; Fedoroff, P.; Bradford, J. -P.; Dassylva, B.; Bouchard, S.
In: Nonlinear Dynamics, Psychology, and Life Sciences, vol. 14, no. 4, pp. 463–489, 2010, ISSN: 10900578.
Abstract | Links | BibTeX | Tags: adult, Arousal, article, behavior, computer interface, Computer Simulation, Computer-Assisted, Erotica, eye movement, Eye movements, human, Humans, Intention, male, mathematical computing, Middle Aged, Nonlinear Dynamics, nonlinear system, pathophysiology, Pedophilia, Penis, physiology, Plethysmography, psychological aspect, Psychomotor Performance, publication, reference value, Reference Values, Sexual Behavior, Signal processing, User-Computer Interface, vascularization
@article{renaud_sexual_2010,
title = {Sexual affordances, perceptual-motor invariance extraction and intentional nonlinear dynamics: Sexually deviant and non-deviant patterns in male subjects},
author = {P. Renaud and M. Goyette and S. Chartier and S. Zhornitski and D. Trottier and J. -L. Rouleau and J. Proulx and P. Fedoroff and J. -P. Bradford and B. Dassylva and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049436590&partnerID=40&md5=f7c928ae6a9624c1c3704748a20f03ec},
issn = {10900578},
year = {2010},
date = {2010-01-01},
journal = {Nonlinear Dynamics, Psychology, and Life Sciences},
volume = {14},
number = {4},
pages = {463–489},
abstract = {Sexual arousal and gaze behavior dynamics are used to characterize deviant sexual interests in male subjects. Pedophile patients and non-deviant subjects are immersed with virtual characters depicting relevant sexual features. Gaze behavior dynamics as indexed from correlation dimensions (D2) appears to be fractal in nature and significantly different from colored noise (surrogate data tests and recurrence plot analyses were performed). This perceptual-motor fractal dynamics parallels sexual arousal and differs from pedophiles to non-deviant subjects when critical sexual information is processed. Results are interpreted in terms of sexual affordance, perceptual invariance extraction and intentional nonlinear dynamics. © 2010 Society for Chaos Theory in Psychology & Life Sciences.},
keywords = {adult, Arousal, article, behavior, computer interface, Computer Simulation, Computer-Assisted, Erotica, eye movement, Eye movements, human, Humans, Intention, male, mathematical computing, Middle Aged, Nonlinear Dynamics, nonlinear system, pathophysiology, Pedophilia, Penis, physiology, Plethysmography, psychological aspect, Psychomotor Performance, publication, reference value, Reference Values, Sexual Behavior, Signal processing, User-Computer Interface, vascularization},
pubstate = {published},
tppubtype = {article}
}
Sexual arousal and gaze behavior dynamics are used to characterize deviant sexual interests in male subjects. Pedophile patients and non-deviant subjects are immersed with virtual characters depicting relevant sexual features. Gaze behavior dynamics as indexed from correlation dimensions (D2) appears to be fractal in nature and significantly different from colored noise (surrogate data tests and recurrence plot analyses were performed). This perceptual-motor fractal dynamics parallels sexual arousal and differs from pedophiles to non-deviant subjects when critical sexual information is processed. Results are interpreted in terms of sexual affordance, perceptual invariance extraction and intentional nonlinear dynamics. © 2010 Society for Chaos Theory in Psychology & Life Sciences.
7.
Baus, O.; Bouchard, S.
The sense of olfaction: Its characteristics and its possible applications in virtual environments Journal Article
In: Journal of Cyber Therapy and Rehabilitation, vol. 3, no. 1, pp. 31–50, 2010, ISSN: 17849934.
Abstract | Links | BibTeX | Tags: Arousal, article, brain region, conditioning, drug dependence, emotionality, human, Learning, leisure, Memory, mental function, neuroanatomy, odor, olfactory discrimination, olfactory system, pain assessment, posttraumatic stress disorder, sex difference, smelling, social interaction, virtual reality, visual stimulation
@article{baus_sense_2010,
title = {The sense of olfaction: Its characteristics and its possible applications in virtual environments},
author = {O. Baus and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650054636&partnerID=40&md5=959d26e8022423056fe9a9345b5ba084},
issn = {17849934},
year = {2010},
date = {2010-01-01},
journal = {Journal of Cyber Therapy and Rehabilitation},
volume = {3},
number = {1},
pages = {31–50},
abstract = {Virtual environments (VE) aim to reproduce life-like experiences, but despite indications that the olfactory sense plays a significant role in everyday life, the integration of olfactory stimuli in VEs is rare. The aim of this paper is to review the literature on olfaction and its potential applications in Virtual Reality (VR). Indications supporting the integration of odorants in VR include the privileged connections between the olfactory system and the brain regions involved in the processing of virtual stimuli used in clinical applications, as well as the interaction between odors, the other senses, and various psychological processes. Presently, smells are mostly integrated in VR applications for post-traumatic stress disorder and drug addiction, but further uses of odorants in VEs could include pain distraction, various training scenarios, such as emergency response and relaxation, and investigations of multi-sensory integration. ©Virtual Reality Medical Institute.},
keywords = {Arousal, article, brain region, conditioning, drug dependence, emotionality, human, Learning, leisure, Memory, mental function, neuroanatomy, odor, olfactory discrimination, olfactory system, pain assessment, posttraumatic stress disorder, sex difference, smelling, social interaction, virtual reality, visual stimulation},
pubstate = {published},
tppubtype = {article}
}
Virtual environments (VE) aim to reproduce life-like experiences, but despite indications that the olfactory sense plays a significant role in everyday life, the integration of olfactory stimuli in VEs is rare. The aim of this paper is to review the literature on olfaction and its potential applications in Virtual Reality (VR). Indications supporting the integration of odorants in VR include the privileged connections between the olfactory system and the brain regions involved in the processing of virtual stimuli used in clinical applications, as well as the interaction between odors, the other senses, and various psychological processes. Presently, smells are mostly integrated in VR applications for post-traumatic stress disorder and drug addiction, but further uses of odorants in VEs could include pain distraction, various training scenarios, such as emergency response and relaxation, and investigations of multi-sensory integration. ©Virtual Reality Medical Institute.
8.
Côté, S.; Bouchard, S.
Cognitive mechanisms underlying virtual reality exposure Journal Article
In: Cyberpsychology and Behavior, vol. 12, no. 2, pp. 121–129, 2009, ISSN: 10949313 (ISSN).
Abstract | Links | BibTeX | Tags: Adaptation, adult, aged, Animals, arachnophobia, Arousal, article, avoidance behavior, cardiovascular response, clinical article, cognition, Culture, female, Heart Rate, human, Humans, Implosive Therapy, male, Middle Aged, Personality Assessment, phobia, Phobic Disorders, prediction, Psychological, questionnaire, Questionnaires, regression analysis, scoring system, Self Efficacy, spider, Spiders, structured interview, task performance, treatment outcome, User-Computer Interface, virtual reality, Young Adult
@article{cote_cognitive_2009,
title = {Cognitive mechanisms underlying virtual reality exposure},
author = {S. Côté and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-64749106909&doi=10.1089%2fcpb.2008.0008&partnerID=40&md5=e9bd263ea9e1940b66910d9651bd119e},
doi = {10.1089/cpb.2008.0008},
issn = {10949313 (ISSN)},
year = {2009},
date = {2009-01-01},
journal = {Cyberpsychology and Behavior},
volume = {12},
number = {2},
pages = {121–129},
abstract = {Many studies have assessed virtual reality exposures efficacy, but very few examined its treatment processes. The addition of objective measures of arousal and information processing mechanisms would be a valuable contribution in order to provide a more complete and detailed picture. The goal of this study was to better document the cognitive mechanisms associated with therapeutic change after an in virtuo exposure treatment. Twenty-eight adults suffering from arachnophobia were assessed and received an exposure-based treatment using virtual reality. General outcome and specific processes measures included a battery of standardized questionnaires, a pictorial emotional Stroop task, a Behavioral Avoidance Test, and a measure of participants' cardiac response while they looked at a live tarantula. The analyses showed that changes in perceived self-efficacy and dysfunctional beliefs were the best predictors of change in general outcome and cardiac response; change in dysfunctional beliefs were the best predictor of change in behavioral avoidance. These innovative results provide a very detailed and organized picture of the complex cognitive mechanisms involved in therapeutic change following in virtuo exposure for arachnophobia. © 2009 Mary Ann Liebert, Inc.},
keywords = {Adaptation, adult, aged, Animals, arachnophobia, Arousal, article, avoidance behavior, cardiovascular response, clinical article, cognition, Culture, female, Heart Rate, human, Humans, Implosive Therapy, male, Middle Aged, Personality Assessment, phobia, Phobic Disorders, prediction, Psychological, questionnaire, Questionnaires, regression analysis, scoring system, Self Efficacy, spider, Spiders, structured interview, task performance, treatment outcome, User-Computer Interface, virtual reality, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Many studies have assessed virtual reality exposures efficacy, but very few examined its treatment processes. The addition of objective measures of arousal and information processing mechanisms would be a valuable contribution in order to provide a more complete and detailed picture. The goal of this study was to better document the cognitive mechanisms associated with therapeutic change after an in virtuo exposure treatment. Twenty-eight adults suffering from arachnophobia were assessed and received an exposure-based treatment using virtual reality. General outcome and specific processes measures included a battery of standardized questionnaires, a pictorial emotional Stroop task, a Behavioral Avoidance Test, and a measure of participants' cardiac response while they looked at a live tarantula. The analyses showed that changes in perceived self-efficacy and dysfunctional beliefs were the best predictors of change in general outcome and cardiac response; change in dysfunctional beliefs were the best predictor of change in behavioral avoidance. These innovative results provide a very detailed and organized picture of the complex cognitive mechanisms involved in therapeutic change following in virtuo exposure for arachnophobia. © 2009 Mary Ann Liebert, Inc.
9.
Côté, S.; Bouchard, S.
Documenting the efficacy of virtual realityexposure with psychophysiological and information processing measures Journal Article
In: Applied Psychophysiology Biofeedback, vol. 30, no. 3, pp. 217–232, 2005, ISSN: 10900586 (ISSN).
Abstract | Links | BibTeX | Tags: adult, analysis of variance, Animals, arachnophobia, Arousal, article, avoidance behavior, cardiovascular response, clinical article, emotion, Emotions, exposure, Fear, female, Heart Rate, human, Humans, information processing, male, medical documentation, Mental Processes, Middle Aged, outcomes research, phobia, phobias, Phobic Disorders, Psychophysiology, questionnaire, Reproducibility of Results, self report, spider, Spiders, standardization, stimulus response, task performance, threat, treatment outcome, User-Computer Interface, virtual reality, Visual Perception, visual stimulation
@article{cote_documenting_2005,
title = {Documenting the efficacy of virtual realityexposure with psychophysiological and information processing measures},
author = {S. Côté and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-25144467954&doi=10.1007%2fs10484-005-6379-x&partnerID=40&md5=12308d48c2d79eff9c6446385244c27c},
doi = {10.1007/s10484-005-6379-x},
issn = {10900586 (ISSN)},
year = {2005},
date = {2005-01-01},
journal = {Applied Psychophysiology Biofeedback},
volume = {30},
number = {3},
pages = {217–232},
abstract = {Many outcome studies have been conducted to assess the efficacy of virtual reality in the treatment of specific phobias. However, most studies used self-report data. The addition of objective measures of arousal and information processing mechanisms would be a valuable contribution in order to validate the usefulness of virtual reality in the treatment of anxiety disorders. The goal of this study was to document the impact of virtual reality exposure (VRE) on cardiac response and automatic processing of threatening stimuli. Twenty-eight adults suffering from arachnophobia were assessed and received an exposure-based treatment using virtual reality. General outcome and specific processes measures included a battery of standardized questionnaires, a pictorial emotional Stroop task, a behavioral avoidance test and a measure of participants' inter-beat intervals (IBI) while they were looking at a live tarantula. Assessment was conducted before and after treatment. Repeated measures ANOVAs revealed that therapy had a positive impact on questionnaire data, as well as on the behavioral avoidance test. Analyses made on the pictorial Stroop task showed that information processing of spider-related stimuli changed after treatment, which also indicates therapeutic success. Psychophysiological data also showed a positive change after treatment, suggesting a decrease in anxiety. In sum, VRE led to significant therapeutic improvements on objective measures as well as on self-report instruments. © 2005 Springer Science+Business Media, Inc.},
keywords = {adult, analysis of variance, Animals, arachnophobia, Arousal, article, avoidance behavior, cardiovascular response, clinical article, emotion, Emotions, exposure, Fear, female, Heart Rate, human, Humans, information processing, male, medical documentation, Mental Processes, Middle Aged, outcomes research, phobia, phobias, Phobic Disorders, Psychophysiology, questionnaire, Reproducibility of Results, self report, spider, Spiders, standardization, stimulus response, task performance, threat, treatment outcome, User-Computer Interface, virtual reality, Visual Perception, visual stimulation},
pubstate = {published},
tppubtype = {article}
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Many outcome studies have been conducted to assess the efficacy of virtual reality in the treatment of specific phobias. However, most studies used self-report data. The addition of objective measures of arousal and information processing mechanisms would be a valuable contribution in order to validate the usefulness of virtual reality in the treatment of anxiety disorders. The goal of this study was to document the impact of virtual reality exposure (VRE) on cardiac response and automatic processing of threatening stimuli. Twenty-eight adults suffering from arachnophobia were assessed and received an exposure-based treatment using virtual reality. General outcome and specific processes measures included a battery of standardized questionnaires, a pictorial emotional Stroop task, a behavioral avoidance test and a measure of participants' inter-beat intervals (IBI) while they were looking at a live tarantula. Assessment was conducted before and after treatment. Repeated measures ANOVAs revealed that therapy had a positive impact on questionnaire data, as well as on the behavioral avoidance test. Analyses made on the pictorial Stroop task showed that information processing of spider-related stimuli changed after treatment, which also indicates therapeutic success. Psychophysiological data also showed a positive change after treatment, suggesting a decrease in anxiety. In sum, VRE led to significant therapeutic improvements on objective measures as well as on self-report instruments. © 2005 Springer Science+Business Media, Inc.