Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

A robot hand testbed designed for enhancing embodiment and functional neurorehabilitation of body schema in subjects with upper limb impairment or loss.

Many upper limb amputees experience an incessant, post-amputation "phantom limb pain" and report that their missing limbs feel paralyzed in an uncomfortable posture. One hypothesis is that efferent commands no longer generate expected afferent signals, such as proprioceptive feedback from changes in limb configuration, and that the mismatch of motor commands and visual feedback is interpreted as pain. Non-invasive therapeutic techniques for treating phantom limb pain, such as mirror visual feedback (MVF), rely on visualizations of postural changes. Advances in neural interfaces for artificial sensory feedback now make it possible to combine MVF with a high-tech "rubber hand" illusion, in which subjects develop a sense of embodiment with a fake hand when subjected to congruent visual and somatosensory feedback. We discuss clinical benefits that could arise from the confluence of known concepts such as MVF and the rubber hand illusion, and new technologies such as neural interfaces for sensory feedback and highly sensorized robot hand testbeds, such as the "BairClaw" presented here. Our multi-articulating, anthropomorphic robot testbed can be used to study proprioceptive and tactile sensory stimuli during physical finger-object interactions. Conceived for artificial grasp, manipulation, and haptic exploration, the BairClaw could also be used for future studies on the neurorehabilitation of somatosensory disorders due to upper limb impairment or loss. A remote actuation system enables the modular control of tendon-driven hands. The artificial proprioception system enables direct measurement of joint angles and tendon tensions while temperature, vibration, and skin deformation are provided by a multimodal tactile sensor. The provision of multimodal sensory feedback that is spatiotemporally consistent with commanded actions could lead to benefits such as reduced phantom limb pain, and increased prosthesis use due to improved functionality and reduced cognitive burden

Enhancement of Robot-Assisted Rehabilitation Outcomes of Post-Stroke Patients Using Movement-Related Cortical Potential

Post-stroke rehabilitation is essential for stroke survivors to help them regain independence and to improve their quality of life. Among various rehabilitation strategies, robot-assisted rehabilitation is an efficient method that is utilized more and more in clinical practice for motor recovery of post-stroke patients. However, excessive assistance from robotic devices during rehabilitation sessions can make patients perform motor training passively with minimal outcome. Towards the development of an efficient rehabilitation strategy, it is necessary to ensure the active participation of subjects during training sessions. This thesis uses the Electroencephalography (EEG) signal to extract the Movement-Related Cortical Potential (MRCP) pattern to be used as an indicator of the active engagement of stroke patients during rehabilitation training sessions. The MRCP pattern is also utilized in designing an adaptive rehabilitation training strategy that maximizes patients’ engagement. This project focuses on the hand motor recovery of post-stroke patients using the AMADEO rehabilitation device (Tyromotion GmbH, Austria). AMADEO is specifically developed for patients with fingers and hand motor deficits. The variations in brain activity are analyzed by extracting the MRCP pattern from the acquired EEG data during training sessions. Whereas, physical improvement in hand motor abilities is determined by two methods. One is clinical tests namely Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) which include FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements’ tests. The other method is the measurement of hand-kinematic parameters using the AMADEO assessment tool which contains hand strength measurements during flexion (force-flexion), and extension (force-extension), and Hand Range of Movement (HROM)

Neuromechanical Biomarkers for Robotic Neurorehabilitation

: One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the "biomarkers" that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the "Rehabilomics" has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective

An integrative framework for tailoring virtual reality based motor rehabilitation after stroke

Stroke is a leading cause of life-lasting motor impairments, undermining the quality of life of stroke survivors and their families, and representing a major chal lenge for a world population that is ageing at a dramatic rate. Important technologi cal developments and neuroscientific discoveries have contributed to a better under standing of stroke recovery. Virtual Reality (VR) arises as a powerful tool because it allows merging contributions from engineering, human computer interaction, reha bilitation medicine and neuroscience to propose novel and more effective paradigms for motor rehabilitation. However, despite evidence of the benefits of these novel training paradigms, most of them still rely on the choice of particular technologi cal solutions tailored to specific subsets of patients. Here we present an integrative framework that utilizes concepts of human computer confluence to 1) enable VR neu rorehabilitation through interface technologies, making VR rehabilitation paradigms accessible to wide populations of patients, and 2) create VR training environments that allow the personalization of training to address the individual needs of stroke patients. The use of these features is demonstrated in pilot studies using VR training environments in different configurations: as an online low-cost version, with a myo electric robotic orthosis, and in a neurofeedback paradigm. Finally, we argue about the need of coupling VR approaches and neurocomputational modelling to further study stroke and its recovery process, aiding on the design of optimal rehabilitation programs tailored to the requirements of each user.info:eu-repo/semantics/publishedVersio

Development of a Unique Whole-Brain Model for Upper Extremity Neuroprosthetic Control

Neuroprostheses are at the forefront of upper extremity function restoration. However, contemporary controllers of these neuroprostheses do not adequately address the natural brain strategies related to planning, execution and mediation of upper extremity movements. These lead to restrictions in providing complete and lasting restoration of function. This dissertation develops a novel whole-brain model of neuronal activation with the goal of providing a robust platform for an improved upper extremity neuroprosthetic controller. Experiments (N=36 total) used goal-oriented upper extremity movements with real-world objects in an MRI scanner while measuring brain activation during functional magnetic resonance imaging (fMRI). The resulting data was used to understand neuromotor strategies using brain anatomical and temporal activation patterns. The study\u27s fMRI paradigm is unique and the use of goal-oriented movements and real-world objects are crucial to providing accurate information about motor task strategy and cortical representation of reaching and grasping. Results are used to develop a novel whole-brain model using a machine learning algorithm. When tested on human subject data, it was determined that the model was able to accurately distinguish functional motor tasks with no prior knowledge. The proof of concept model created in this work should lead to improved prostheses for the treatment of chronic upper extremity physical dysfunction

VALIDATION OF A MODEL OF SENSORIMOTOR INTEGRATION WITH CLINICAL BENEFITS

Healthy sensorimotor integration – or how our touch influences our movements – is critical to efficiently interact with our environment. Yet, many aspects of this process are still poorly understood. Importantly, several movement disorders are often considered as originating from purely motor impairments, while a sensory origin could also lead to a similar set of symptoms. To alleviate these issues, we hereby propose a novel biologically-based model of the sensorimotor loop, known as the SMILE model. After describing both the functional, and the corresponding neuroanatomical versions of the SMILE, we tested several aspects of its motor component through functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS). Both experimental studies resulted in coherent outcomes with respect to the SMILE predictions, but they also provided novel scientific outcomes about such broad topics as the sub-phases of motor imagery, the neural processing of bodily representations, or the extend of the role of the extrastriate body area. In the final sections of this manuscript, we describe some potential clinical application of the SMILE. The first one presents the identification of plausible neuroanatomical origins for focal hand dystonia, a yet poorly understood sensorimotor disorder. The last chapter then covers possible improvements on brain-machine interfaces, driven by a better understanding of the sensorimotor system. -- La façon dont votre sens du toucher et vos mouvements interagissent est connue sous le nom d’intégration sensorimotrice. Ce procédé est essentiel pour une interaction normale avec tout ce qui nous entoure. Cependant, plusieurs aspects de ce processus sont encore méconnus. Plus important encore, l’origine de certaines déficiences motrices encore trop peu comprises sont parfois considérées comme purement motrice, alors qu’une origine sensorielle pourrait mener à un même ensemble de symptômes. Afin d’améliorer cette situation, nous proposons ici un nouveau modèle d’intégration sensorimotrice, dénommé « SMILE », basé sur les connaissances de neurobiologie actuelles. Dans ce manuscrit, nous commençons par décrire les caractéristiques fonctionnelles et neuroanatomiques du SMILE. Plusieurs expériences sont ensuite effectuées, via l’imagerie par résonance magnétique fonctionnelle (IRMf), et la stimulation magnétique transcranienne (SMT), afin de tester différents aspects de la composante motrice du SMILE. Si les résultats de ces expériences corroborent les prédictions du SMILE, elles ont aussi mis en évidences d’autres résultats scientifiques intéressants et novateurs, dans des domaines aussi divers que les sous-phases de l’imagination motrice, les processus cérébraux liés aux représentations corporelles, ou encore l’extension du rôle de l’extrastriate body area. Dans les dernières parties de ce manuscrit, nous dévoilons quelques applications cliniques potentielles de notre modèle. Nous utilisons le SMILE afin de proposer deux origines cérébrales plausibles de la dystonie focale de la main. Le dernier chapitre présente comment certaines technologies existantes, telles que les interfaces cerveaux-machines, pourraient bénéficier d’une meilleure compréhension du système sensorimoteur