Brain computer interface based robotic rehabilitation with online modification of task speed

We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile - a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies

Control of a BCI-based upper limb rehabilitation system utilizing posterior probabilities (BBA tabanlı üst uzuv rehabilitasyon sisteminin sonsal olasılık değerleri kullanılarak kontrolü)

In this paper, an electroencephalogram (EEG) based Brain-Computer Interface (BCI) is integrated with a robotic system designed to target rehabilitation therapies of stroke patients such that patients can control the rehabilitation robot by imagining movements of their right arm. In particular, the power density of frequency bands are used as features from the EEG signals recorded during the experiments and they are classified by Linear Discriminant Analysis (LDA). As one of the novel contributions of this study, the posterior probabilities extracted from the classifier are directly used as the continuous-valued outputs, instead of the discrete classification output commonly used by BCI systems, to control the speed of the therapeutic movements performed by the robotic system. Adjusting the exercise speed of patients online, as proposed in this study, according to the instantaneous levels of motor imagery during the movement, has the potential to increase efficacy of robot assisted therapies by ensuring active involvement of patients. The proposed BCI-based robotic rehabilitation system has been successfully implemented on physical setups in our laboratory and sample experimental data are presented

Design, implementation and BCI-based control of a series elastic mobile robot for home-based physical rehabilitation

We present the design, control and human-machine interface of AssistOn- Mobile, a series elastic holonomic mobile platform aimed to administer therapeutic table-top exercises to patients who have suffered injuries that affect the function of their upper extremities. AssistOn-Mobile is designed as a multi-DoF series elastic actuator based on a holonomic mobile platform consisting of four actuated Mecanum wheels. Thanks to its mobile base, it is a compact and portable device that can cover whole human workspace for planar reaching exercises. Even though the mobile platform is not passively backdriveable due to the Mecanum wheels, a low-cost, compliant mechanism based, multi-DoF series elastic element is introduced to transform the holonomic mobile platform into a series elastic actuator and to ensure actively backdriveability of the overall system. Implementation of series elastic actuation results in many advantages, such as eliminating the need for costly force sensors, improving the performance of force control, and increasing the impact resistance and robustness of the overall system. For the control of AssistOn-Mobile, in addition to admittance controllers used for active backdriveability, passive velocity eld control (PVFC) is implemented for contour following tasks. PVFC minimizes the contour error by decoupling the task (path tracking) and the timing of the task, while also ensuring the coupled stability of the human-in-the-loop system by rendering the system passive with respect to externally applied forces. Furthermore, since AssistOn-Mobile is an end-effector type device, patients' shoulder movements are continually tracked utilizing a Kinect (RGBD) sensor such that compensatory movements of the patients (e.g, leaning) are limited by providing online feedback to the patients (for instance, by modulating the speed of the contour tracking task). With these controllers in place, AssistOn-Mobile becomes a highly backdriveable, force-controlled robotic interface that can provide required amount of assistance/ resistance to patients, while performing omni-directional movements on a plane. To enable patients with severe disabilities (e.g., spinal cord injury patients with no residual movements on their affected limb) to interact with AssistOn- Mobile and to provide assist-as-needed rehabilitation protocols to such patients, we introduce a systematic approach for online modi cation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In one implementation, we utilize posterior probabilities extracted from an LDA classiffier as the continuous-valued outputs to PFVC. This way, PVFC modulates the speed of contour following tasks with respect to intention levels of motor imagery. The efficacy of our proposed robotic BCI framework with online modification of task speed is investigated by a set of human subject experiments with healthy volunteers. In particular, our approach is compared with the existing BCI-based virtual reality and robot-assisted rehabilitation techniques. Within this experiment, we have collected statistically significant evidence of the beneficial effect of the haptic feedback during the mental imagery of subjects. Results also indicate that using BCI continuously rather than to initialize the movement only may be preferable to ensure active participation of patients throughout the therapy. Finally, using the proposed BCI-based rehabilitation protocol shows no statistically signi cant difference in terms of mental imagery activity, compared to the rehabilitation protocol where the subjects are actively performing the real movement

AssistOn-Mobile: A series elastic holonomic mobile platform for upper extremity rehabilitation

We present the design and control of series elastic holonomic mobile platform, ASSISTON-MOBILE, aimed to administer therapeutic table-top exercises to patients who have suffered injuries that affect the function of their upper extremities. The proposed mobile platform is a low-cost, portable, easy-to-use rehabilitation device for home use. ASSISTON-MOBILE consists of four actuated Mecanum wheels and a compliant, low-cost, multi degree-of-freedom Series Elastic Element as its force sensing unit. Thanks to its series elastic actuation, ASSISTON-MOBILE is highly back-driveable and can provide assistance/resistance to patients, while performing omni-directional movements on plane. Feasibility tests and preliminary usability studies with the robot are presented. The device holds promise in improving accuracy and effectiveness of repetitive movement therapies completed at home, while also providing quantitative measures of patient progress

AssistOn-Mobile: A series elastic holonomic mobile platform for upper extremity rehabilitation

We present the design, control, and human–machine interface of a series elastic holonomic mobile platform, AssistOn-Mobile, aimed to administer therapeutic table-top exercises to patients who have suffered injuries that affect the function of their upper extremities. The proposed mobile platform is a low-cost, portable, easy-to-use rehabilitation device targeted for home use. In particular, AssistOn-Mobile consists of a holonomic mobile platform with four actuated Mecanum wheels and a compliant, low-cost, multi-degrees-of-freedom series elastic element acting as its force sensing unit. Thanks to its series elastic actuation, AssistOn-Mobile is highly backdriveable and can provide assistance/resistance to patients, while performing omni-directional movements on plane. AssistOn-Mobile also features Passive Velocity Field Control (PVFC) to deliver human-in-the-loop contour tracking rehabilitation exercises. PVFC allows patients to complete the contour-tracking tasks at their preferred pace, while providing the proper amount of assistance as determined by the therapists. PVFC not only minimizes the contour error but also does so by rendering the closed-loop system passive with respect to externally applied forces; hence, ensures the coupled stability of the human-robot system. We evaluate the feasibility and effectiveness of AssistOn-Mobile with PVFC for rehabilitation and present experimental data collected during human subject experiments under three case studies. In particular, we utilize AssistOn-Mobile with PVFC (a) to administer contour following tasks where the pace of the tasks is left to the control of the patients, so that the patients can assume a natural and comfortable speed for the tasks, (b) to limit compensatory movements of the patients by integrating a RGB-D sensor to the system to continually monitor the movements of the patients and to modulate the task speeds to provide online feedback to the patients, and (c) to integrate a Brain–Computer Interface such that the brain activity of the patients is mapped to the robot speed along the contour following tasks, rendering an assist-as-needed protocol for the patients with severe disabilities. The feasibility studies indicate that AssistOn-Mobile holds promise in improving the accuracy and effectiveness of repetitive movement therapies, while also providing quantitative measures of patient progress

Design and comparative evaluation of a BCI-based upper extremity robotic rehabilitation protocol

We advocate online modification of robot-assisted task speed, based on continuously inferred motor imagery as an effective rehabilitation protocol for increasing the involvement levels of the patients in physical rehabilitation exercises. To study efficacy of such Brain-Computer Interface (BCI) based physical rehabilitation protocols, we conduct human subject experiments on healthy volunteers, comparing several BCI-based protocols with haptic and visual feedback with each other and with conventional robot-assisted rehabilitation protocols, in terms of intensity and sustainability of motor imagery. Our results provide evidence that the online adjusted BCI-based robotic protocol helps subjects produce stronger and more sustained motor imagery throughout the motor task, compared to other BCI-based protocols. We also show that BCI-assisted robotic therapy can enable a level of motor cortical activity that is similar to a scenario in which the subjects could actually execute the motion. These results suggest that BCI-assisted rehabilitation methods that provide online modification of the task speed based on continuously inferred motor imagery have potential in increasing the level of involvement of patients during exercises and may lead to more effective recovery