A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

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Brain Computer Interfaces, a Review

This paper introduces a newly developed gait rehabilitation device. The device, called LOPES, combines a freely translatable and 2-D-actuated pelvis segment with a leg exoskeleton containing three actuated rotational joints: two at the hip and one at the knee. The joints are impedance controlled to allow bidirectional mechanical interaction between the robot and the training subject. Evaluation measurements show that the device allows both a "pa- tient-in-charge" and "robot-in-charge" mode, in which the robot is controlled either to follow or to guide a patient, respectively. Electromyography (EMG) measurements (one subject) on eight important leg muscles, show that free walking in the device strongly resembles free treadmill walking; an indication that the device can offer task-specific gait training. The possibilities and limitations to using the device as gait measurement tool are also shown at the moment position measurements are not accurate enough for inverse-dynamical gait analysis.

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Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.

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Review of control strategies for robotic movement training after neurologic injury

It is generally accepted that augmented feedback, provided by a human expert or a technical display, effectively enhances motor learning. However, discussion of the way to most effectively provide augmented feedback has been controversial. Related studies have focused primarily on simple or artificial tasks enhanced by visual feedback. Recently, technical advances have made it possible also to investigate more complex, realistic motor tasks and to implement not only visual, but also auditory, haptic, or multimodal augmented feedback. The aim of this review is to address the potential of augmented unimodal and multimodal feedback in the framework of motor learning theories. The review addresses the reasons for the different impacts of feedback strategies within or between the visual, auditory, and haptic modalities and the challenges that need to be overcome to provide appropriate feedback in these modalities, either in isolation or in combination. Accordingly, the design criteria for successful visual, auditory, haptic, and multimodal feedback are elaborated.

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Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review

Technological advancements have led to the development of numerous wearable robotic devices for the physical assistance and restoration of human locomotion. While many challenges remain with respect to the mechanical design of such devices, it is at least equally challenging and important to develop strategies to control them in concert with the intentions of the user. This work reviews the state-of-the-art techniques for controlling portable active lower limb prosthetic and orthotic (P/O) devices in the context of locomotive activities of daily living (ADL), and considers how these can be interfaced with the user’s sensory-motor control system. This review underscores the practical challenges and opportunities associated with P/O control, which can be used to accelerate future developments in this field. Furthermore, this work provides a classification scheme for the comparison of the various control strategies. As a novel contribution, a general framework for the control of portable gait-assistance devices is proposed. This framework accounts for the physical and informatic interactions between the controller, the user, the environment, and the mechanical device itself. Such a treatment of P/Os – not as independent devices, but as actors within an ecosystem – is suggested to be necessary to structure the next generation of intelligent and multifunctional controllers. Each element of the proposed framework is discussed with respect to the role that it plays in the assistance of locomotion, along with how its states can be sensed as inputs to the controller. The reviewed controllers are shown to fit within different levels of a hierarchical scheme, which loosely resembles the structure and functionality of the nominal human central nervous system (CNS). Active and passive safety mechanisms are considered to be central aspects underlying all of P/O design and control, and are shown to be critical for regulatory approval of such devices for real-world use. The works discussed herein provide evidence that, while we are getting ever closer, significant challenges still exist for the development of controllers for portable powered P/O devices that can seamlessly integrate with the user’s neuromusculoskeletal system and are practical for use in locomotive ADL.

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Control strategies for active lower extremity prosthetics and orthotics: a review

Task-oriented repetitive movements can improve motor performance in patients with neurological or orthopaedic lesions. The application of robotics and automation technology can serve to assist, enhance, evaluate, and document neurological and orthopedic rehabilitation. This paper deals with the application of "patient-cooperative" techniques to robot-aided gait rehabilitation of neurological disorders. We define patient-cooperative to mean that, during movement, the technical system takes into account the patient's intention and voluntary efforts rather than imposing any predefined movements or inflexible strategies. It is hypothesized that such cooperative robotic approaches can improve the therapeutic outcome compared to classical rehabilitation strategies. New cooperative strategies are presented that detect the patient's voluntary efforts. First, this enables the patient increased freedom of movement by a certain amount of robot compliance. Second, the robot behavior adapts to the existing voluntary motor abilities. And third, the robotic system displays and improves the patient contribution by visual biofeedback. Initial experimental results are presented to evaluate the basic principle and technical function of proposed approaches. Further improvements of the technical design and additional clinical testing is required to prove whether the therapeutic outcome can be enhanced by such cooperative strategies.

Patient-cooperative strategies for robot-aided treadmill training: first experimental results

Gait rehabilitation robots are of increasing importance in neurorehabilitation. Conventional devices are often criticized because they are limited to reproducing predefined movement patterns. Research on patient-cooperative control strategies aims at improving robotic behavior. Robots should support patients only as much as needed and stimulate them to produce maximal voluntary efforts. This paper presents a patient-cooperative strategy that allows patients to influence the timing of their leg movements along a physiologically meaningful path. In this ?path control? strategy, compliant virtual walls keep the patient's legs within a ?tunnel? around the desired spatial path. Additional supportive torques enable patients to move along the path with reduced effort. Graphical feedback provides visual training instructions. The path control strategy was evaluated with 10 healthy subjects and 15 subjects with incomplete spinal cord injury. The spatio-temporal characteristics of recorded kinematic data showed that subjects walked with larger temporal variability with the new strategy. Electromyographic data indicated that subjects were training more actively. A majority of iSCI subjects was able to actively control their gait timing. Thus, the strategy allows patients to train walking while being helped rather than controlled by the robot.

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Path Control: A Method for Patient-Cooperative Robot-Aided Gait Rehabilitation

Background
Development and increasing acceptance of rehabilitation robots as well as advances in technology allow new forms of therapy for patients with neurological disorders Robot-assisted gait therapy can increase the training duration and the intensity for the patients while reducing the physical strain for the therapist

Optimal training effects during gait therapy generally depend on appropriate feedback about performance Compared to manual treadmill therapy, there is a loss of physical interaction between therapist and patient with robotic gait retraining Thus, it is difficult for the therapist to assess the necessary feedback and instructions The aim of this study was to define a biofeedback system for a gait training robot and test its usability in subjects without neurological disorders

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Biofeedback for robotic gait rehabilitation.

It is known that improvement in walking function can be achieved in patients suffering a movement disorder after stroke or spinal cord injury by providing intensive locomotor training. Rehabilitation robots allow for a longer and more intensive training than that achieved by conventional therapies. Robot assisted treadmill training also offers the ability to provide objective feedback within one training session and to monitor functional improvements over time. This article provides an overview of the technical features and reports the clinical data available for one of these systems known as "Lokomat". First, background information is given for the neural mechanisms of gait recovery. The basic technical approach of the Lokomat system is then described. Furthermore, new features are introduced including cooperative control strategies, assessment tools and augmented feedback. These features may be capable of further enhancing training intensity and patient participation. Findings from clinical studies are presented covering the feasibility as well as efficacy of Lokomat assisted treadmill training.

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Locomotor training in subjects with sensori-motor deficits: An overview of the robotic gait orthosis lokomat

Robot-aided gait training can increase the duration and number of training sessions while reducing the number of therapists required for each patient. However, current automated gait trainers do not adapt their movement to the patient's muscular efforts and passive musculoskeletal properties. Furthermore, robot-aided training without therapists lacks the feedback required for patient assessment. In this article, we present results from the literature and our research to provide an overview of novel human-centered strategies for robot behaviors that are patient-cooperative and support motor-function assessment. Combining robot-aided training with robot-aided assessment will likely make future gait therapy easier, more comfortable, and more efficient. Broad clinical testing is still required for proving this assumption.

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Lars Lünenburger

Papers

Patient-cooperative strategies for robot-aided treadmill training: first experimental results

Path Control: A Method for Patient-Cooperative Robot-Aided Gait Rehabilitation

Biofeedback for robotic gait rehabilitation.

Locomotor training in subjects with sensori-motor deficits: An overview of the robotic gait orthosis lokomat

Human-centered robotics applied to gait training and assessment.