Co-adaptive control strategies in assistive Brain-Machine Interfaces

A large number of people with severe motor disabilities cannot access any of the available control inputs of current assistive products, which typically rely on residual motor functions. These patients are therefore unable to fully benefit from existent assistive technologies, including communication interfaces and assistive robotics. In this context, electroencephalography-based Brain-Machine Interfaces (BMIs) offer a potential non-invasive solution to exploit a non-muscular channel for communication and control of assistive robotic devices, such as a wheelchair, a telepresence robot, or a neuroprosthesis. Still, non-invasive BMIs currently suffer from limitations, such as lack of precision, robustness and comfort, which prevent their practical implementation in assistive technologies. The goal of this PhD research is to produce scientific and technical developments to advance the state of the art of assistive interfaces and service robotics based on BMI paradigms. Two main research paths to the design of effective control strategies were considered in this project. The first one is the design of hybrid systems, based on the combination of the BMI together with gaze control, which is a long-lasting motor function in many paralyzed patients. Such approach allows to increase the degrees of freedom available for the control. The second approach consists in the inclusion of adaptive techniques into the BMI design. This allows to transform robotic tools and devices into active assistants able to co-evolve with the user, and learn new rules of behavior to solve tasks, rather than passively executing external commands. Following these strategies, the contributions of this work can be categorized based on the typology of mental signal exploited for the control. These include: 1) the use of active signals for the development and implementation of hybrid eyetracking and BMI control policies, for both communication and control of robotic systems; 2) the exploitation of passive mental processes to increase the adaptability of an autonomous controller to the user\u2019s intention and psychophysiological state, in a reinforcement learning framework; 3) the integration of brain active and passive control signals, to achieve adaptation within the BMI architecture at the level of feature extraction and classification

Clinical assessment of the TechArm system on visually impaired and blind children during uni- and multi-sensory perception tasks

We developed the TechArm system as a novel technological tool intended for visual rehabilitation settings. The system is designed to provide a quantitative assessment of the stage of development of perceptual and functional skills that are normally vision-dependent, and to be integrated in customized training protocols. Indeed, the system can provide uni- and multisensory stimulation, allowing visually impaired people to train their capability of correctly interpreting non-visual cues from the environment. Importantly, the TechArm is suitable to be used by very young children, when the rehabilitative potential is maximal. In the present work, we validated the TechArm system on a pediatric population of low-vision, blind, and sighted children. In particular, four TechArm units were used to deliver uni- (audio or tactile) or multi-sensory stimulation (audio-tactile) on the participant's arm, and subject was asked to evaluate the number of active units. Results showed no significant difference among groups (normal or impaired vision). Overall, we observed the best performance in tactile condition, while auditory accuracy was around chance level. Also, we found that the audio-tactile condition is better than the audio condition alone, suggesting that multisensory stimulation is beneficial when perceptual accuracy and precision are low. Interestingly, we observed that for low-vision children the accuracy in audio condition improved proportionally to the severity of the visual impairment. Our findings confirmed the TechArm system's effectiveness in assessing perceptual competencies in sighted and visually impaired children, and its potential to be used to develop personalized rehabilitation programs for people with visual and sensory impairments

Investigating cardiovascular and cerebrovascular variability in postural syncope by means of extended Granger causality

The patterns of Granger causality (GC) between heart period (HP), mean arterial pressure (AP) and cerebral blood flow velocity (FV) were investigated in ten subjects with postural related syncope (PRS). The classic GC measure based on vector autoregressive (VAR) modeling was compared with a novel extended GC (eGC) measure derived from VAR models incorporating instantaneous causal effects among the series. The analysis was performed in the supine and in the upright position during early (ET) and late (LT, close to presyncope) epochs of head-up tilt. Moving from ET to LT, both GC and eGC decreased from AP to HP, and increased from AP to FV, reflecting baroreflex impairment and loss of cerebral autoregulation. The statistical significance of these changes was better assessed using the eGC, thus suggesting the importance of including instantaneous effects in the causality analysis of cardiovascular and cerebrovascular variability during PRS