Francisco Resquín

Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions

BackgroundAs a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes.MethodsIn this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user’s brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection.ResultsThree healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively).ConclusionsThe accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.

Hybrid robotic systems for upper limb rehabilitation after stroke: A review

In recent years the combined use of functional electrical stimulation (FES) and robotic devices, called hybrid robotic rehabilitation systems, has emerged as a promising approach for rehabilitation of lower and upper limb motor functions. This paper presents a review of the state of the art of current hybrid robotic solutions for upper limb rehabilitation after stroke. For this aim, studies have been selected through a search using web databases: IEEE-Xplore, Scopus and PubMed. A total of 10 different hybrid robotic systems were identified, and they are presented in this paper. Selected systems are critically compared considering their technological components and aspects that form part of the hybrid robotic solution, the proposed control strategies that have been implemented, as well as the current technological challenges in this topic. Additionally, we will present and discuss the corresponding evidences on the effectiveness of these hybrid robotic therapies. The review also discusses the future trends in this field.

Feedback error learning controller for functional electrical stimulation assistance in a hybrid robotic system for reaching rehabilitation

Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.

Motor Control and Learning Theories

Patients who have suffered impairment of their neuromotor abilities due to a disease or accident have to relearn to control their bodies. For example, after stroke the ability to coordinate the movements of the upper limb in order to reach and grasp an object could be severely damaged. Or in the case of amputees, the functional ability is completely lost.

Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient

Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the users movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the users movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.

Task Influence on Motor-Related Cortical Signals: Comparison Between Upper and Lower Limb Coordinated and Analytic Movements

Changes in cortical signals related to motor planning processes have been widely studied in the past. However, no studies so far have investigated the intra-subject differences in these signals between analytical and coordinated upper limb and lower limb cue-based movements. Here, data from healthy subjects is analyzed to research this aspect. The statistical analysis carried out with data from 7 subjects indicates that statistically significant differences were observed between premotor cortical activities of upper and lower limb movements. Specifically, higher amplitudes of the contingent negative variation pattern were observed for lower-limb tasks. Such results may be due to complexity in movement task planning. BCI devices could take advantage and be improved with the knowledge provided.

Brain-Machine Interface and Functional Electrical Stimulation for Cycling Increases Corticospinal Excitability in a Stroke Patient: A Case Study

Various experimental strategies using brain-machine interfaces, both for the upper limb and the lower limb, have managed to generate plastic changes in the nervous system aiming to rehabilitate diseases involving movement restrictions. Due to the variability in the results of previous studies and the lack of experiments with associative facilitation, more interventions are necessary with functional tasks, involving different lower limb muscles, trying to rehabilitate patients with these pathologies. In this study, we present data of a stroke patient in which an intervention with a cycling task was studied. The intervention consists in a brain machine interface, which has been integrated driving a functional electrical stimulation device. Increases in corticospinal excitability of both Tibialis anterior muscles of the patient were observed after the intervention. Such results could imply that the brain machine interface would be behind these changes, which would help to rehabilitate these patients.

Hybrid Robotic System for Reaching Rehabilitation After Stroke: Reporting an Usability Experimentation

The combined use of functional electrical stimulation and robotic exoskeleton in a hybrid rehabilitation system represents a promising research field for rehabilitation of the motor functions after stroke. In this work, we report the results obtained in a study carried out with a hybrid robotic system for reaching rehabilitation. The system was tested in two sessions with one chronic stroke subject.

A Brain-Machine Interface Architecture to Control an Upper Limb Rehabilitation Exoskeleton

The combination of Brain-Machine Interfaces (BMIs) with assistive technologies has seen a rapid development during the last few years. Regarding post-stroke rehabilitation, the BMI could be combined with other sensors to assist movements performed by the patient by attaching an exoskeleton to the affected arm. To that end, the patient’s arm movement intentions can be obtained by processing the brain information and generating suitable output commands to control the exoskeleton kinematics. In this paper, we propose an architecture that combines a Brain-Machine Interface with an upper limb exoskeleton. Two different experimental setups based on flexion/extension movements of the arm are proposed.