3,211 research outputs found

    Navigation in Real-World Environments: New Opportunities Afforded by Advances in Mobile Brain Imaging

    Get PDF
    A central question in neuroscience and psychology is how the mammalian brain represents the outside world and enables interaction with it. Significant progress on this question has been made in the domain of spatial cognition, where a consistent network of brain regions that represent external space has been identified in both humans and rodents. In rodents, much of the work to date has been done in situations where the animal is free to move about naturally. By contrast, the majority of work carried out to date in humans is static, due to limitations imposed by traditional laboratory based imaging techniques. In recent years, significant progress has been made in bridging the gap between animal and human work by employing virtual reality (VR) technology to simulate aspects of real-world navigation. Despite this progress, the VR studies often fail to fully simulate important aspects of real-world navigation, where information derived from self-motion is integrated with representations of environmental features and task goals. In the current review article, we provide a brief overview of animal and human imaging work to date, focusing on commonalties and differences in findings across species. Following on from this we discuss VR studies of spatial cognition, outlining limitations and developments, before introducing mobile brain imaging techniques and describe technical challenges and solutions for real-world recording. Finally, we discuss how these advances in mobile brain imaging technology, provide an unprecedented opportunity to illuminate how the brain represents complex multifaceted information during naturalistic navigation

    Navigation in real-world environments : new opportunities afforded by advances in mobile brain imaging

    Get PDF
    A central question in neuroscience and psychology is how the mammalian brain represents the outside world and enables interaction with it. Significant progress on this question has been made in the domain of spatial cognition, where a consistent network of brain regions that represent external space has been identified in both humans and rodents. In rodents, much of the work to date has been done in situations where the animal is free to move about naturally. By contrast, the majority of work carried out to date in humans is static, due to limitations imposed by traditional laboratory based imaging techniques. In recent years, significant progress has been made in bridging the gap between animal and human work by employing virtual reality (VR) technology to simulate aspects of real-world navigation. Despite this progress, the VR studies often fail to fully simulate important aspects of real-world navigation, where information derived from self-motion is integrated with representations of environmental features and task goals. In the current review article, we provide a brief overview of animal and human imaging work to date, focusing on commonalties and differences in findings across species. Following on from this we discuss VR studies of spatial cognition, outlining limitations and developments, before introducing mobile brain imaging techniques and describe technical challenges and solutions for real-world recording. Finally, we discuss how these advances in mobile brain imaging technology, provide an unprecedented opportunity to illuminate how the brain represents complex multifaceted information during naturalistic navigation.Publisher PDFPeer reviewe

    Sculpting Unrealities: Using Machine Learning to Control Audiovisual Compositions in Virtual Reality

    Get PDF
    This thesis explores the use of interactive machine learning (IML) techniques to control audiovisual compositions within the emerging medium of virtual reality (VR). Accompanying the text is a portfolio of original compositions and open-source software. These research outputs represent the practical elements of the project that help to shed light on the core research question: how can IML techniques be used to control audiovisual compositions in VR? In order to find some answers to this question, it was broken down into its constituent elements. To situate the research, an exploration of the contemporary field of audiovisual art locates the practice between the areas of visual music and generative AV. This exploration of the field results in a new method of categorising the constituent practices. The practice of audiovisual composition is then explored, focusing on the concept of equality. It is found that, throughout the literature, audiovisual artists aim to treat audio and visual material equally. This is interpreted as a desire for balance between the audio and visual material. This concept is then examined in the context of VR. A feeling of presence is found to be central to this new medium and is identified as an important consideration for the audiovisual composer in addition to the senses of sight and sound. Several new terms are formulated which provide the means by which the compositions within the portfolio are analysed. A control system, based on IML techniques, is developed called the Neural AV Mapper. This is used to develop a compositional methodology through the creation of several studies. The outcomes from these studies are incorporated into two live performance pieces, Ventriloquy I and Ventriloquy II. These pieces showcase the use of IML techniques to control audiovisual compositions in a live performance context. The lessons learned from these pieces are incorporated into the development of the ImmersAV toolkit. This open-source software toolkit was built specifically to allow for the exploration of the IML control paradigm within VR. The toolkit provides the means by which the immersive audiovisual compositions, Obj_#3 and Ag Fás Ar Ais Arís are created. Obj_#3 takes the form of an immersive audiovisual sculpture that can be manipulated in real-time by the user. The title of the thesis references the physical act of sculpting audiovisual material. It also refers to the ability of VR to create alternate realities that are not bound to the physics of real-life. This exploration of unrealities emerges as an important aspect of the medium. The final piece in the portfolio, Ag Fás Ar Ais Arís takes the knowledge gained from the earlier work and pushes the boundaries to maximise the potential of the medium and the material

    Real Virtuality: A Code of Ethical Conduct. Recommendations for Good Scientific Practice and the Consumers of VR-Technology

    Get PDF
    The goal of this article is to present a first list of ethical concerns that may arise from research and personal use of virtual reality (VR) and related technology, and to offer concrete recommendations for minimizing those risks. Many of the recommendations call for focused research initiatives. In the first part of the article, we discuss the relevant evidence from psychology that motivates our concerns. In Section “Plasticity in the Human Mind,” we cover some of the main results suggesting that one’s environment can influence one’s psychological states, as well as recent work on inducing illusions of embodiment. Then, in Section “Illusions of Embodiment and Their Lasting Effect,” we go on to discuss recent evidence indicating that immersion in VR can have psychological effects that last after leaving the virtual environment. In the second part of the article, we turn to the risks and recommendations. We begin, in Section “The Research Ethics of VR,” with the research ethics of VR, covering six main topics: the limits of experimental environments, informed consent, clinical risks, dual-use, online research, and a general point about the limitations of a code of conduct for research. Then, in Section “Risks for Individuals and Society,” we turn to the risks of VR for the general public, covering four main topics: long-term immersion, neglect of the social and physical environment, risky content, and privacy. We offer concrete recommendations for each of these 10 topics, summarized in Table 1

    Resting-state fMRI activity predicts unsupervised learning and memory in an immersive virtual reality environment

    Get PDF
    In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment

    Machine Learning and Virtual Reality on Body MovementsÂż Behaviors to Classify Children with Autism Spectrum Disorder

    Full text link
    [EN] Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements' frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients' subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements' biomarkers that could contribute to improving ASD diagnosis.This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness funded project "Immersive virtual environment for the evaluation and training of children with autism spectrum disorder: T Room" (IDI-20170912) and by the Generalitat Valenciana funded project REBRAND (PROMETEO/2019/105). Furthermore, this work was co-founded by the European Union through the Operational Program of the European Regional development Fund (ERDF) of the Valencian Community 2014-2020 (IDIFEDER/2018/029).Alcañiz Raya, ML.; Marín-Morales, J.; Minissi, ME.; Teruel Garcia, G.; Abad, L.; Chicchi-Giglioli, IA. (2020). Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder. Journal of Clinical Medicine. 9(5):1-20. https://doi.org/10.3390/jcm9051260S12095https://www.who.int/news-room/fact-sheets/detail/autism-spectrum-disordersAnagnostou, E., Zwaigenbaum, L., Szatmari, P., Fombonne, E., Fernandez, B. A., Woodbury-Smith, M., … Scherer, S. W. (2014). Autism spectrum disorder: advances in evidence-based practice. Canadian Medical Association Journal, 186(7), 509-519. doi:10.1503/cmaj.121756Lord, C., Risi, S., DiLavore, P. S., Shulman, C., Thurm, A., & Pickles, A. (2006). Autism From 2 to 9 Years of Age. Archives of General Psychiatry, 63(6), 694. doi:10.1001/archpsyc.63.6.694Schmidt, L., Kirchner, J., Strunz, S., Broźus, J., Ritter, K., Roepke, S., & Dziobek, I. (2015). Psychosocial Functioning and Life Satisfaction in Adults With Autism Spectrum Disorder Without Intellectual Impairment. Journal of Clinical Psychology, 71(12), 1259-1268. doi:10.1002/jclp.22225Turner, M. (1999). Annotation: Repetitive Behaviour in Autism: A Review of Psychological Research. Journal of Child Psychology and Psychiatry, 40(6), 839-849. doi:10.1111/1469-7610.00502Lewis, M. H., & Bodfish, J. W. (1998). Repetitive behavior disorders in autism. Mental Retardation and Developmental Disabilities Research Reviews, 4(2), 80-89. doi:10.1002/(sici)1098-2779(1998)4:23.0.co;2-0Mahone, E. M., Bridges, D., Prahme, C., & Singer, H. S. (2004). Repetitive arm and hand movements (complex motor stereotypies) in children. The Journal of Pediatrics, 145(3), 391-395. doi:10.1016/j.jpeds.2004.06.014MacDonald, R., Green, G., Mansfield, R., Geckeler, A., Gardenier, N., Anderson, J., … Sanchez, J. (2007). Stereotypy in young children with autism and typically developing children. Research in Developmental Disabilities, 28(3), 266-277. doi:10.1016/j.ridd.2006.01.004Singer, H. S. (2009). Motor Stereotypies. Seminars in Pediatric Neurology, 16(2), 77-81. doi:10.1016/j.spen.2009.03.008Lidstone, J., Uljarević, M., Sullivan, J., Rodgers, J., McConachie, H., Freeston, M., … Leekam, S. (2014). Relations among restricted and repetitive behaviors, anxiety and sensory features in children with autism spectrum disorders. Research in Autism Spectrum Disorders, 8(2), 82-92. doi:10.1016/j.rasd.2013.10.001GOLDMAN, S., WANG, C., SALGADO, M. W., GREENE, P. E., KIM, M., & RAPIN, I. (2009). Motor stereotypies in children with autism and other developmental disorders. Developmental Medicine & Child Neurology, 51(1), 30-38. doi:10.1111/j.1469-8749.2008.03178.xLord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659-685. doi:10.1007/bf02172145Volkmar, F. R., State, M., & Klin, A. (2009). Autism and autism spectrum disorders: diagnostic issues for the coming decade. Journal of Child Psychology and Psychiatry, 50(1-2), 108-115. doi:10.1111/j.1469-7610.2008.02010.xReaven, J. A., Hepburn, S. L., & Ross, R. G. (2008). Use of the ADOS and ADI-R in Children with Psychosis: Importance of Clinical Judgment. Clinical Child Psychology and Psychiatry, 13(1), 81-94. doi:10.1177/1359104507086343Torres, E. B., Brincker, M., Isenhower, R. W., Yanovich, P., Stigler, K. A., Nurnberger, J. I., … José, J. V. (2013). Autism: the micro-movement perspective. Frontiers in Integrative Neuroscience, 7. doi:10.3389/fnint.2013.00032Möricke, E., Buitelaar, J. K., & Rommelse, N. N. J. (2015). Do We Need Multiple Informants When Assessing Autistic Traits? The Degree of Report Bias on Offspring, Self, and Spouse Ratings. Journal of Autism and Developmental Disorders, 46(1), 164-175. doi:10.1007/s10803-015-2562-yCHAYTOR, N., SCHMITTEREDGECOMBE, M., & BURR, R. (2006). Improving the ecological validity of executive functioning assessment. Archives of Clinical Neuropsychology, 21(3), 217-227. doi:10.1016/j.acn.2005.12.002Brunswik, E. (1955). Representative design and probabilistic theory in a functional psychology. Psychological Review, 62(3), 193-217. doi:10.1037/h0047470Gillberg, C., & Rasmussen, P. (1994). Brief report: Four case histories and a literature review of williams syndrome and autistic behavior. Journal of Autism and Developmental Disorders, 24(3), 381-393. doi:10.1007/bf02172235Parsons, S. (2016). Authenticity in Virtual Reality for assessment and intervention in autism: A conceptual review. Educational Research Review, 19, 138-157. doi:10.1016/j.edurev.2016.08.001Francis, K. (2005). Autism interventions: a critical update. Developmental Medicine & Child Neurology, 47(7), 493-499. doi:10.1017/s0012162205000952Albinali, F., Goodwin, M. S., & Intille, S. S. (2009). Recognizing stereotypical motor movements in the laboratory and classroom. Proceedings of the 11th international conference on Ubiquitous computing. doi:10.1145/1620545.1620555Pyles, D. A. M., Riordan, M. M., & Bailey, J. S. (1997). The stereotypy analysis: An instrument for examining environmental variables associated with differential rates of stereotypic behavior. Research in Developmental Disabilities, 18(1), 11-38. doi:10.1016/s0891-4222(96)00034-0Nosek, B. A., Hawkins, C. B., & Frazier, R. S. (2011). Implicit social cognition: from measures to mechanisms. Trends in Cognitive Sciences, 15(4), 152-159. doi:10.1016/j.tics.2011.01.005Forscher, P. S., Lai, C. K., Axt, J. R., Ebersole, C. R., Herman, M., Devine, P. G., & Nosek, B. A. (2019). A meta-analysis of procedures to change implicit measures. Journal of Personality and Social Psychology, 117(3), 522-559. doi:10.1037/pspa0000160LeDoux, J. E., & Pine, D. S. (2016). Using Neuroscience to Help Understand Fear and Anxiety: A Two-System Framework. American Journal of Psychiatry, 173(11), 1083-1093. doi:10.1176/appi.ajp.2016.16030353Fenning, R. M., Baker, J. K., Baucom, B. R., Erath, S. A., Howland, M. A., & Moffitt, J. (2017). Electrodermal Variability and Symptom Severity in Children with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders, 47(4), 1062-1072. doi:10.1007/s10803-016-3021-0Nikula, R. (1991). Psychological Correlates of Nonspecific Skin Conductance Responses. Psychophysiology, 28(1), 86-90. doi:10.1111/j.1469-8986.1991.tb03392.xAlcañiz Raya, M., Chicchi Giglioli, I. A., Marín-Morales, J., Higuera-Trujillo, J. L., Olmos, E., Minissi, M. E., … Abad, L. (2020). Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality. Frontiers in Human Neuroscience, 14. doi:10.3389/fnhum.2020.00090Cunningham, W. A., Raye, C. L., & Johnson, M. K. (2004). Implicit and Explicit Evaluation: fMRI Correlates of Valence, Emotional Intensity, and Control in the Processing of Attitudes. Journal of Cognitive Neuroscience, 16(10), 1717-1729. doi:10.1162/0898929042947919Kopton, I. M., & Kenning, P. (2014). Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00549Nickel, P., & Nachreiner, F. (2003). Sensitivity and Diagnosticity of the 0.1-Hz Component of Heart Rate Variability as an Indicator of Mental Workload. Human Factors: The Journal of the Human Factors and Ergonomics Society, 45(4), 575-590. doi:10.1518/hfes.45.4.575.27094Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., … Milham, M. P. (2013). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659-667. doi:10.1038/mp.2013.78Van Hecke, A. V., Lebow, J., Bal, E., Lamb, D., Harden, E., Kramer, A., … Porges, S. W. (2009). Electroencephalogram and Heart Rate Regulation to Familiar and Unfamiliar People in Children With Autism Spectrum Disorders. Child Development, 80(4), 1118-1133. doi:10.1111/j.1467-8624.2009.01320.xCoronato, A., De Pietro, G., & Paragliola, G. (2014). A situation-aware system for the detection of motion disorders of patients with Autism Spectrum Disorders. Expert Systems with Applications, 41(17), 7868-7877. doi:10.1016/j.eswa.2014.05.011Goodwin, M. S., Intille, S. S., Albinali, F., & Velicer, W. F. (2010). Automated Detection of Stereotypical Motor Movements. Journal of Autism and Developmental Disorders, 41(6), 770-782. doi:10.1007/s10803-010-1102-zRodrigues, J. L., Gonçalves, N., Costa, S., & Soares, F. (2013). Stereotyped movement recognition in children with ASD. Sensors and Actuators A: Physical, 202, 162-169. doi:10.1016/j.sna.2013.04.019Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., & Castiglioni, I. (2015). Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities. Journal of Autism and Developmental Disorders, 45(7), 2146-2156. doi:10.1007/s10803-015-2379-8Wedyan, M., Al-Jumaily, A., & Crippa, A. (2019). Using machine learning to perform early diagnosis of Autism Spectrum Disorder based on simple upper limb movements. International Journal of Hybrid Intelligent Systems, 15(4), 195-206. doi:10.3233/his-190278Parsons, S., Mitchell, P., & Leonard, A. (2004). The Use and Understanding of Virtual Environments by Adolescents with Autistic Spectrum Disorders. Journal of Autism and Developmental Disorders, 34(4), 449-466. doi:10.1023/b:jadd.0000037421.98517.8dParsons, T. D., Rizzo, A. A., Rogers, S., & York, P. (2009). Virtual reality in paediatric rehabilitation: A review. Developmental Neurorehabilitation, 12(4), 224-238. doi:10.1080/17518420902991719Bowman, D. A., Gabbard, J. L., & Hix, D. (2002). A Survey of Usability Evaluation in Virtual Environments: Classification and Comparison of Methods. Presence: Teleoperators and Virtual Environments, 11(4), 404-424. doi:10.1162/105474602760204309Pastorelli, E., & Herrmann, H. (2013). A Small-scale, Low-budget Semi-immersive Virtual Environment for Scientific Visualization and Research. Procedia Computer Science, 25, 14-22. doi:10.1016/j.procs.2013.11.003Cobb, S. V. G., Nichols, S., Ramsey, A., & Wilson, J. R. (1999). Virtual Reality-Induced Symptoms and Effects (VRISE). Presence: Teleoperators and Virtual Environments, 8(2), 169-186. doi:10.1162/105474699566152Wallace, S., Parsons, S., Westbury, A., White, K., White, K., & Bailey, A. (2010). Sense of presence and atypical social judgments in immersive virtual environments. Autism, 14(3), 199-213. doi:10.1177/1362361310363283Lorenzo, G., Lledó, A., Arráez-Vera, G., & Lorenzo-Lledó, A. (2018). The application of immersive virtual reality for students with ASD: A review between 1990–2017. Education and Information Technologies, 24(1), 127-151. doi:10.1007/s10639-018-9766-7Bailenson, J. N., Yee, N., Merget, D., & Schroeder, R. (2006). The Effect of Behavioral Realism and Form Realism of Real-Time Avatar Faces on Verbal Disclosure, Nonverbal Disclosure, Emotion Recognition, and Copresence in Dyadic Interaction. Presence: Teleoperators and Virtual Environments, 15(4), 359-372. doi:10.1162/pres.15.4.359Cipresso, P., Giglioli, I. A. C., Raya, M. A., & Riva, G. (2018). The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature. Frontiers in Psychology, 9. doi:10.3389/fpsyg.2018.02086Cummings, J. J., & Bailenson, J. N. (2015). How Immersive Is Enough? A Meta-Analysis of the Effect of Immersive Technology on User Presence. Media Psychology, 19(2), 272-309. doi:10.1080/15213269.2015.1015740Skalski, P., & Tamborini, R. (2007). The Role of Social Presence in Interactive Agent-Based Persuasion. Media Psychology, 10(3), 385-413. doi:10.1080/15213260701533102Slater, M. (2009). Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3549-3557. doi:10.1098/rstb.2009.0138Baños, R. M., Botella, C., Garcia-Palacios, A., Villa, H., Perpiña, C., & Alcañiz, M. (2000). Presence and Reality Judgment in Virtual Environments: A Unitary Construct? CyberPsychology & Behavior, 3(3), 327-335. doi:10.1089/10949310050078760Bente, G., Rüggenberg, S., Krämer, N. C., & Eschenburg, F. (2008). Avatar-Mediated Networking: Increasing Social Presence and Interpersonal Trust in Net-Based Collaborations. Human Communication Research, 34(2), 287-318. doi:10.1111/j.1468-2958.2008.00322.xHeeter, C. (1992). Being There: The Subjective Experience of Presence. Presence: Teleoperators and Virtual Environments, 1(2), 262-271. doi:10.1162/pres.1992.1.2.262Sanchez-Vives, M. V., & Slater, M. (2005). From presence to consciousness through virtual reality. Nature Reviews Neuroscience, 6(4), 332-339. doi:10.1038/nrn1651Mohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G., & Klinkman, M. (2013). Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health. General Hospital Psychiatry, 35(4), 332-338. doi:10.1016/j.genhosppsych.2013.03.008Neguț, A., Matu, S.-A., Sava, F. A., & David, D. (2016). Virtual reality measures in neuropsychological assessment: a meta-analytic review. The Clinical Neuropsychologist, 30(2), 165-184. doi:10.1080/13854046.2016.1144793Riva, G. (2005). Virtual Reality in Psychotherapy: Review. CyberPsychology & Behavior, 8(3), 220-230. doi:10.1089/cpb.2005.8.220Valmaggia, L. R., Latif, L., Kempton, M. J., & Rus-Calafell, M. (2016). Virtual reality in the psychological treatment for mental health problems: An systematic review of recent evidence. Psychiatry Research, 236, 189-195. doi:10.1016/j.psychres.2016.01.015Mesa-Gresa, P., Gil-Gómez, H., Lozano-Quilis, J.-A., & Gil-Gómez, J.-A. (2018). Effectiveness of Virtual Reality for Children and Adolescents with Autism Spectrum Disorder: An Evidence-Based Systematic Review. Sensors, 18(8), 2486. doi:10.3390/s18082486Cheng, Y., & Ye, J. (2010). Exploring the social competence of students with autism spectrum conditions in a collaborative virtual learning environment – The pilot study. Computers & Education, 54(4), 1068-1077. doi:10.1016/j.compedu.2009.10.011Jarrold, W., Mundy, P., Gwaltney, M., Bailenson, J., Hatt, N., McIntyre, N., … Swain, L. (2013). Social Attention in a Virtual Public Speaking Task in Higher Functioning Children With Autism. Autism Research, 6(5), 393-410. doi:10.1002/aur.1302Forgeot d’Arc, B., Ramus, F., Lefebvre, A., Brottier, D., Zalla, T., Moukawane, S., … Delorme, R. (2014). Atypical Social Judgment and Sensitivity to Perceptual Cues in Autism Spectrum Disorders. Journal of Autism and Developmental Disorders, 46(5), 1574-1581. doi:10.1007/s10803-014-2208-5Maskey, M., Lowry, J., Rodgers, J., McConachie, H., & Parr, J. R. (2014). Reducing Specific Phobia/Fear in Young People with Autism Spectrum Disorders (ASDs) through a Virtual Reality Environment Intervention. PLoS ONE, 9(7), e100374. doi:10.1371/journal.pone.0100374Baron-Cohen, S., Ashwin, E., Ashwin, C., Tavassoli, T., & Chakrabarti, B. (2009). Talent in autism: hyper-systemizing, hyper-attention to detail and sensory hypersensitivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1522), 1377-1383. doi:10.1098/rstb.2008.0337Tomchek, S. D., Huebner, R. A., & Dunn, W. (2014). Patterns of sensory processing in children with an autism spectrum disorder. Research in Autism Spectrum Disorders, 8(9), 1214-1224. doi:10.1016/j.rasd.2014.06.006Boyd, B. A., Baranek, G. T., Sideris, J., Poe, M. D., Watson, L. R., Patten, E., & Miller, H. (2010). Sensory features and repetitive behaviors in children with autism and developmental delays. Autism Research, n/a-n/a. doi:10.1002/aur.124Gabriels, R. L., Agnew, J. A., Miller, L. J., Gralla, J., Pan, Z., Goldson, E., … Hooks, E. (2008). Is there a relationship between restricted, repetitive, stereotyped behaviors and interests and abnormal sensory response in children with autism spectrum disorders? Research in Autism Spectrum Disorders, 2(4), 660-670. doi:10.1016/j.rasd.2008.02.002Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021). OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172-186. doi:10.1109/tpami.2019.2929257Schölkopf, 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:10.1145/1961189.1961199O’Neill, M., & Jones, R. S. P. (1997). Journal of Autism and Developmental Disorders, 27(3), 283-293. doi:10.1023/a:1025850431170Foss-Feig, J. H., Kwakye, L. D., Cascio, C. J., Burnette, C. P., Kadivar, H., Stone, W. L., & Wallace, M. T. (2010). An extended multisensory temporal binding window in autism spectrum disorders. Experimental Brain Research, 203(2), 381-389. doi:10.1007/s00221-010-2240-4Courchesne, E., Lincoln, A. J., Kilman, B. A., & Galambos, R. (1985). Event-related brain potential correlates of the processing of novel visual and auditory information in autism. Journal of Autism and Developmental Disorders, 15(1), 55-76. doi:10.1007/bf01837899Russo, N., Foxe, J. J., Brandwein, A. B., Altschuler, T., Gomes, H., & Molholm, S. (2010). Multisensory processing in children with autism: high-density electrical mapping of auditory-somatosensory integration. Autism Research, 3(5), 253-267. doi:10.1002/aur.152Ament, K., Mejia, A., Buhlman, R., Erklin, S., Caffo, B., Mostofsky, S., & Wodka, E. (2014). Evidence for Specificity of Motor Impairments in Catching and Balance in Children with Autism. Journal of Autism and Developmental Disorders, 45(3), 742-751. doi:10.1007/s10803-014-2229-

    The Hybrid BCI

    Get PDF
    Nowadays, everybody knows what a hybrid car is. A hybrid car normally has two engines to enhance energy efficiency and reduce CO2 output. Similarly, a hybrid brain-computer interface (BCI) is composed of two BCIs, or at least one BCI and another system. A hybrid BCI, like any BCI, must fulfill the following four criteria: (i) the device must rely on signals recorded directly from the brain; (ii) there must be at least one recordable brain signal that the user can intentionally modulate to effect goal-directed behaviour; (iii) real time processing; and (iv) the user must obtain feedback. This paper introduces hybrid BCIs that have already been published or are in development. We also introduce concepts for future work. We describe BCIs that classify two EEG patterns: one is the event-related (de)synchronisation (ERD, ERS) of sensorimotor rhythms, and the other is the steady-state visual evoked potential (SSVEP). Hybrid BCIs can either process their inputs simultaneously, or operate two systems sequentially, where the first system can act as a “brain switch”. For example, we describe a hybrid BCI that simultaneously combines ERD and SSVEP BCIs. We also describe a sequential hybrid BCI, in which subjects could use a brain switch to control an SSVEP-based hand orthosis. Subjects who used this hybrid BCI exhibited about half the false positives encountered while using the SSVEP BCI alone. A brain switch can also rely on hemodynamic changes measured through near-infrared spectroscopy (NIRS). Hybrid BCIs can also use one brain signal and a different type of input. This additional input can be an electrophysiological signal such as the heart rate, or a signal from an external device such as an eye tracking system

    Decoding subjective emotional arousal from EEG during an immersive Virtual Reality experience

    Get PDF
    Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation:37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a Long Short-Term Memory recurrent neural network.We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience.Competing Interest StatementThe authors have declared no competing interest

    Sensory Spaces

    Get PDF
    We are all products of our environments and simultaneously have the ability to shape and change those environments. Physical environments obviously influence how we perceive and understand ourselves in relation to our surroundings, but non-physical environments have an ever-increasing effect as well. Changing technologies and increased use of online networks pose new questions about how we understand and relate to the settings we inhabit. Inspired by the work of Sherry Turkle, my work examines the tension in the transition many people experience as they incorporate more internet-ready, globally connected technology into their daily lives. I examine the intersection between virtual and physical spaces. My work focuses on the advantages and disadvantages, specifically as seen through the lens of the other, looking for new perspectives concerning our roles as we occupy each simultaneously. Materially, I draw from found objects and unscripted recorded observations to describe physical spaces I\u27ve encountered, as well as utilizing technological possibilities for describing virtual environments. The collision of the physical and non-physical made manifest in large-scale installations creates hybrid virtual/tangible environments. Viewers of these environments are made aware of their presence in relationship to the work though layers of sensory stimuli and are often offered interactive possibilities to explore. This experience creates new knowledge about our increasingly multilayered society
    • …
    corecore