Brain–computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum

Individuals with Autism Spectrum Disorder (ASD) show deficits in social and communicative skills, including imitation, empathy, and shared attention, as well as restricted interests and repetitive patterns of behaviors. Evidence for and against the idea that dysfunctions in the mirror neuron system are involved in imitation and could be one underlying cause for ASD is discussed in this review. Neurofeedback interventions have reduced symptoms in children with ASD by self-regulation of brain rhythms. However, cortical deficiencies are not the only cause of these symptoms. Peripheral physiological activity, such as the heart rate, is closely linked to neurophysiological signals and associated with social engagement. Therefore, a combined approach targeting the interplay between brain, body and behavior could be more effective. Brain-computer interface applications for combined neurofeedback and biofeedback treatment for children with ASD are currently nonexistent. To facilitate their use, we have designed an innovative game that includes social interactions and provides neural- and body-based feedback that corresponds directly to the underlying significance of the trained signals as well as to the behavior that is reinforced

Corporal diagnostic work and diagnostic spaces: Clinicians' use of space and bodies during diagnosis

© 2015 The Authors. Sociology of Health & Illness © 2015 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.An emerging body of literature in sociology has demonstrated that diagnosis is a useful focal point for understanding the social dimensions of health and illness. This article contributes to this work by drawing attention to the relationship between diagnostic spaces and the way in which clinicians use their own bodies during the diagnostic process. As a case study, we draw upon fieldwork conducted with a multidisciplinary clinical team providing deep brain stimulation (DBS) to treat children with a movement disorder called dystonia. Interviews were conducted with team members and diagnostic examinations were observed. We illustrate that clinicians use communicative body work and verbal communication to transform a material terrain into diagnostic space, and we illustrate how this diagnostic space configures forms of embodied 'sensing-and-acting' within. We argue that a 'diagnosis' can be conceptualised as emerging from an interaction in which space, the clinician-body, and the patient-body (or body-part) mutually configure one another. By conceptualising diagnosis in this way, this article draws attention to the corporal bases of diagnostic power and counters Cartesian-like accounts of clinical work in which the patient-body is objectified by a disembodied medical discourse.The Wellcome Trust (Wellcome Trust Biomedical Strategic Award 086034

Tracking technical refinement in elite performers: The good, the better, and the ugly

This study extends coaching research examining the practical implementation of technical refinement in elite-level golfers. In doing so, we provide an initial check of precepts pertaining to the Five-A Model and, examine the dynamics between coaching, psychomotor, biomechanical and psychological inputs to the process. Three case studies of golfers attempting refinements to their already well-established techniques are reported. Kinematic data were supplemented with intra-individual movement variability and self-perceptions of mental effort as measures of tracking behaviour and motor control. Results showed different levels of success in refining technique and subsequent ability to return to executing under largely subconscious control. In one case, the technique was refined as intended but without consistent reduction of conscious attention, in another, both were successfully apparent, whereas in the third case neither was achieved. Implications of these studies are discussed with reference to the process’ interdisciplinary nature and importance of the initial and final stages

Challenges and Future of Wearable Technology in Human Motor-Skill Learning and Optimization

Learning how to move is a challenging task. Even the most basic motor skill of walking requires years to develop and can quickly deteriorate due to aging and sedentary lifestyles. More specialized skills such as ballet and acrobatic kicks in soccer require “talent” and years of extensive practice to fully master. These practices can easily cause injuries if conducted improperly. 3D motion capture technologies are currently the best way to acquire human motor skill in biomechanical feedback training. Owing to their tremendous promise for a plethora of applications, wearable technologies have garnered great interest in biofeedback training. Using wearable technology, some physical activity parameters can be tracked in real time and a noninvasive way to indicate the physical progress of a trainee. Yet, the application of biomechanical wearables in human motor-skill learning, training, and optimization is still in its infant phase due to the absence of a reliable method. This chapter elaborates challenges faced by developing wearable biomechanical feedback devices and forecasts potential breakthroughs in this area. The overarching goal is to foster interdisciplinary studies on wearable technology to improve how we move

Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data

We report on promising results concerning the fast and accurate diagnosis of developmental coordination disorder (DCD) which heavily impacts the life of affected children with emotional and behavioral issues. Using a machine learning classifier on spectral data of electroencephalography (EEG) recordings and unfolding the traditional frequency bandwidth in a fine-graded equidistant 99-point spectrum we were able to reach an accuracy of over 99.35 percent having only one misclassification. Our machine learning work contributes to healthcare and information systems research. While current diagnostic methods in use are either complicated, time-consuming, or inaccurate, our automated machine-based approach is accurate and reliable. Our results also provide more insights into the relationship between DCD and brain activity which could stimulate future work in medicine

Attention-deficit/hyperactivity disorder: a closer look

Includes bibliographical references