Event-Related EEG Desynchronization Reveals Enhanced Motor Imagery From the Third Person Perspective by Manipulating Sense of Body Ownership With Virtual Reality for Stroke Patients

Virtual reality (VR)-based rehabilitation training holds great potential for post-stroke motor recovery. Existing VR-based motor imagery (MI) paradigms mostly focus on the first-person perspective, and the benefit of the third-person perspective (3PP) remains to be further exploited. The 3PP is advantageous for movements involving the back or those with a large range because of its field coverage. Some movements are easier to imagine from the 3PP. However, the 3PP training efficiency may be unsatisfactory, which may be attributed to the difficulty encountered when generating a strong sense of ownership (SOO). In this work, we attempt to enhance a visual-guided 3PP MI in stroke patients by eliciting the SOO over a virtual avatar with VR. We propose to achieve this by inducing the so-called out-of-body experience (OBE), which is a full-body illusion (FBI) that people misperceive a 3PP virtual body as his/her own (i.e., generating the SOO to the virtual body). Electroencephalography signals of 13 stroke patients are recorded while MI of the affected upper limb is being performed. The proposed paradigm is evaluated by comparing event-related desynchronization (ERD) with a control paradigm without FBI induction. The results show that the proposed paradigm leads to a significantly larger ERD during MI, indicating a bilateral activation pattern consistent with that in previous studies. In conclusion, 3PP MI can be enhanced in stroke patients by eliciting the SOO through induction of the "OBE" FBI. This study offers more possibilities for virtual rehabilitation in stroke patients and can further facilitate VR application in rehabilitation.

Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

Nowadays, the rehabilitation robot has been developed for rehabilitation therapy. However, there are few studies on upper arm exoskeletons for rehabilitation training of muscle strength. This article aims to design a surface electromyography-controlled lightweight exoskeleton rehabilitation robot for home-based progressive resistance training. The exoskeleton’s lightweight structure is designed based on the kinematic model of the elbow joint and ergonomics sizes of the arm. At the same time, the overall weight of the exoskeleton is controlled at only 3.03 kg. According to the rehabilitation training task, we use torque limit mode to ensure stable torque output at variable velocity. We also propose a surface electromyography-based control method, which uses k- Nearest Neighbor algorithm to classify surface electromyographic signals under progressive training loads, and utilizes principal component analysis to improve the recognition accuracy to control the exoskeleton to provide muscle strength compensation. The assessment experiment of the exoskeleton rehabilitation robot shows that the dynamic recognition accuracy of this control method is 80.21%. Muscle activity of biceps brachii and triceps brachii under each training load decreases significantly when subjects with the exoskeleton robot. The results indicate that the exoskeleton rehabilitation robot can output the corresponding torque to assist in progressive resistance training. This study provides a solution to potential problems in the family-oriented application of exoskeleton rehabilitation robots.

A reward–punishment feedback control strategy based on energy information for wrist rehabilitation

Based on evidence from the previous research in rehabilitation robot control strategies, we found that the common feature of the effective control strategies to promote subjects’ engagement is creating a reward–punishment feedback mechanism. This article proposes a reward–punishment feedback control strategy based on energy information. Firstly, an engagement estimated approach based on energy information is developed to evaluate subjects’ performance. Secondly, the estimated result forms a reward–punishment term, which is introduced into a standard model-based adaptive controller. This modified adaptive controller is capable of giving the reward–punishment feedback to subjects according to their engagement. Finally, several experiments are implemented using a wrist rehabilitation robot to evaluate the proposed control strategy with 10 healthy subjects who have not cardiovascular and cerebrovascular diseases. The results of these experiments show that the mean coefficient of determination ( R 2) of the data obtained by the proposed approach and the classical approach is 0.7988, which illustrate the reliability of the engagement estimated approach based on energy information. And the results also demonstrate that the proposed controller has great potential to promote patients’ engagement for wrist rehabilitation.