Physiological Self Regulation with Biofeedback Games

Mental stress is a global epidemic that can have serious health consequences including cardiovascular diseases and diabetes. Several techniques are available to teach stress self-regulation skills including therapy, meditation, deep breathing, and biofeedback. While effective, these methods suffer from high drop-outs due to the monotonic nature of the exercises and are generally practiced in quiet relaxed environment, which may not transfer to real-world scenarios. To address these issues, this dissertation presents a novel intervention for stress training using games and wearable sensors. The approach consists of monitoring the user’s physiological signals during gameplay, mapping them into estimates of stress levels, and adapting the game in a way that promotes states of low arousal. This approach offers two key advantages. First, it allows users to focus on the gameplay rather than on monitoring their physiological signals, which makes the training far more engaging. More importantly, it teaches users to self-regulate their stress response, while performing a task designed to increase arousal. Within this broad framework, this dissertation studies three specific problems. First, the dissertation evaluates three physiological signals (breathing rate, heart rate variability, and electrodermal activity) that span across the dimensions of degrees of selectivity in measuring arousal and voluntary control in their effectiveness in lowering arousal. This will identify the signal appropriate for game based stress training and the associated bio-signal processing techniques for real-time arousal estimation. Second, this dissertation investigates different methods of biofeedback presentation e.g. visual feedback and game adaptation during gameplay. Selection of appropriate biofeedback mechanism is critical since it provides the necessary information to improve the perception of visceral states (e.g. stress) to the user. Furthermore, these modalities facilitate skill acquisition in distinct ways (i.e., top-down and bottom-up learning) and influence retention of skills. Third, this dissertation studies reinforcement scheduling in a game and its effect on skill learning and retention. A reinforcement schedule determines which occurrences of the target response are reinforced. This study focuses on continuous and partial reinforcement schedules in GBF and their effect on resistance to extinction (i.e. ability to retain learned skills) after the biofeedback is removed. The main contribution of this dissertation is in demonstrating that stress self-regulation training can be embedded in videogames and help individuals develop more adaptive responses to reduce physiological stress encountered both at home and work

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Physiological Self Regulation with Biofeedback Games

Mental stress is a global epidemic that can have serious health consequences including cardiovascular diseases and diabetes. Several techniques are available to teach stress self-regulation skills including therapy, meditation, deep breathing, and biofeedback. While effective, these methods suffer from high drop-outs due to the monotonic nature of the exercises and are generally practiced in quiet relaxed environment, which may not transfer to real-world scenarios. To address these issues, this dissertation presents a novel intervention for stress training using games and wearable sensors. The approach consists of monitoring the user’s physiological signals during gameplay, mapping them into estimates of stress levels, and adapting the game in a way that promotes states of low arousal. This approach offers two key advantages. First, it allows users to focus on the gameplay rather than on monitoring their physiological signals, which makes the training far more engaging. More importantly, it teaches users to self-regulate their stress response, while performing a task designed to increase arousal. Within this broad framework, this dissertation studies three specific problems. First, the dissertation evaluates three physiological signals (breathing rate, heart rate variability, and electrodermal activity) that span across the dimensions of degrees of selectivity in measuring arousal and voluntary control in their effectiveness in lowering arousal. This will identify the signal appropriate for game based stress training and the associated bio-signal processing techniques for real-time arousal estimation. Second, this dissertation investigates different methods of biofeedback presentation e.g. visual feedback and game adaptation during gameplay. Selection of appropriate biofeedback mechanism is critical since it provides the necessary information to improve the perception of visceral states (e.g. stress) to the user. Furthermore, these modalities facilitate skill acquisition in distinct ways (i.e., top-down and bottom-up learning) and influence retention of skills. Third, this dissertation studies reinforcement scheduling in a game and its effect on skill learning and retention. A reinforcement schedule determines which occurrences of the target response are reinforced. This study focuses on continuous and partial reinforcement schedules in GBF and their effect on resistance to extinction (i.e. ability to retain learned skills) after the biofeedback is removed. The main contribution of this dissertation is in demonstrating that stress self-regulation training can be embedded in videogames and help individuals develop more adaptive responses to reduce physiological stress encountered both at home and work

Understanding and Supporting Decision-Making in Electronic Auctions: A NeuroIS Approach

Making use of the potential of NeuroIS, I apply a NeuroIS approach in this thesis to further the understanding of decision-making and to analyze the opportunities for NeuroIS in decision-support, both in electronic auctions

Somatics Research Bibliography: A Working Tool for Somatics and Somatic Psychology

Many years ago when Somatics magazine was young, it occurred to me that it would be valuable to collect and publish research article references in Somatics magazine that were relevant to the different somatics disciplines to encourage the development of the field. There were next to no studies devoted to Somatics itself, but there were many studies devoted to the elements of somatic practices. Somatics is a multidisciplinary field. It builds on the research findings from many fields, such as anatomy, physiology, neurophysiology, psychology, dance, biomechanics, and education. The references are selected to be suggestive to the interested researcher and practitioner for their purposes and of the many possible research avenues that are yet to be explored. I have collected these research references for more than four decades. I worked originally with Psychological Abstracts, then PsychInfo, and finally, PubMed. Over that time there has been more research done on the somatic disciplines themselves. The greatest amount of research has been done on yoga (the oldest and largest of the somatic disciplines) and yoga therapy. These studies are examples of the research that can be done with the other somatics disciplines as well. We are in an era that appreciates evidence-based practice and practice-based evidence. This is evidence. These research articles are selected according to the following criteria: The article combines both body and mind either in its research design or theoretical perspective; the research design incorporates convergent measures—that is, it includes physiological, behavioral, and psychological measures; subjective and objective measures; and the research focuses on the whole organism (human) from a somatic perspective—that is, the effect of a body therapy on a psychological state. Topics addressed include biofeedback, body psychotherapy, consciousness states, electrophysiology, kinesiology, mind and body, motor processes, neural basis of motor control, neuroscience, posture and emotion, psychophysiology, and yoga/yoga therapy