EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots

Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as, wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user's mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications' key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It i

A Computational Approach for the Design of Epidural Electrical Spinal Cord Stimulation Strategies to Enable Locomotion after Spinal Cord Injury

Spinal cord injury (SCI) is a major cause of paralysis with currently no effective treatment. Epidural electrical stimulation (EES) of the lumbar spinal cord has been shown to restore locomotion in animal models of SCI, but has not yet reached the same level of efficacy in humans. The mechanisms through which EES promotes locomotion, and the causes underlying these inter-species differences remain largely unknown, although essential to fully exploit the therapeutic potential of this neuromodulation strategy. Here, we addressed these questions using a deductive approach based on computer simulations and hypothesis-driven experiments, and proposed complementary strategies to enhance the current efficacy of EES-based therapies. In the first part of this thesis, we studied the mechanisms through which EES enables locomotion in rat models of SCI. Performing simulations and behavioral experiments, we provided evidence that EES modulates proprioceptive afferents activity, without interfering with the ongoing sensory signals. We showed that this synergistic interaction allows muscle spindle feedback circuits to steer the unspecific excitation delivered by EES to functionally relevant pathways, thus allowing the formation of locomotor patterns. By leveraging this understanding, we developed a stimulation strategy that allowed adjusting lesion-specific gait deficits, hence increasing the therapeutic efficacy of EES. In the second part of this thesis, we evaluated the influence of trunk posture on proprioceptive feedback circuits during locomotion, and thus on the effect of EES, in rat models of SCI. By combining modeling and experiments, we showed that trunk orientation regulates leg proprioceptive signals, as well as the motor patterns produced during EES-induced stepping. We exploited these results to develop a control policy that by automatically regulating trunk orientation significantly enhanced locomotor performance. In the last part of this thesis, we investigated the causes underlying species-specific effects of EES. Hypothesis-driven simulations suggested that in humans continuous EES blocks the proprioceptive signals traveling along the recruited fibers. We corroborated this prediction by performing experiments in rats and people with SCI. In particular, we showed that EES disrupts the conscious perception of leg movements, as well as the afferent modulation of sensorimotor circuits in humans, but not in rats. We provide evidence that in humans, due to this phenomenon, continuous EES can only facilitate locomotion to a limited extent. This was insufficient to provide clinically relevant improvements in the tested participants. Finally, we proposed two sensory-compliant stimulation strategies that might overcome these limitations, and thus augment the therapeutic efficacy of EES. In this thesis we elucidated key mechanisms through which EES promotes locomotion, we exposed critical limitations of continuous EES strategies when applied to humans, and we introduced complementary strategies to maximize the efficacy of EES therapies. These findings have far-reaching implications in the development of future strategies and technologies supporting the recovery of locomotion in people with SCI using EES

New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work