Neurorehabilitation Training Research Articles

Effectiveness of the Use of Virtual Reality Rehabilitation in Children with Dyslexia: Follow-Up after One Year.

Dyslexia is a common learning disorder that hinders reading fluency and comprehension. Traditional treatments can be tedious for children, limiting their effectiveness. This study investigated the one-year effects of rehabilitation treatment with a virtual reality rehabilitation system (VRRS) on children with dyslexia. Twenty-four children were divided into control (CG) and experimental (EG) groups. The CG underwent conventional neuropsychological treatment (CNT), while the EG underwent VR neurorehabilitation training (VRNT) using the VRRS. Neuropsychological evaluation was conducted before treatment, after six months, and again after one year for both groups. Compared to the control group, children who received VR training showed significant improvement in reading skills, especially in non-word reading and reading speed, even after one year without further VR intervention. VRRS can improve treatment adherence and minimize symptoms by offering engaging activities for children. These findings suggest VRRS may be a valuable tool for dyslexia rehabilitation with long-lasting effects.

Current Status of Neurorehabilitation Services in the OIC Countries: Results of an International Online Survey

Objectives: Neurorehabilitation services are not widely available to most persons with disability (PWD) in the Organization of Islamic Cooperation countries. This study aimed to document status of neurorehabilitation services in OIC countries. Study Design and Setting: Cross-sectional online survey. Methods: Self-administered online survey conducted using Google Forms. Key resource persons for each OIC country working in field of neurorehabilitation were identified from various databases. The questionnaire was emailed to 20 identified resource persons. The response rate was 60% (12/20). Results: Neurorehabilitation services were available in 9 out of 12 surveyed countries. Only 2 countries reported providing access to neurorehabilitation services to 76-100% of their population. Five countries reported less than 25% of population had access to these services. Most commonly available neurorehabilitation services included services for stroke, spinal cord injury, pediatric neurorehabilitation, brain injury, and neuromuscular rehabilitation. Three countries had none of these specialized services. Five countries had neurorehabilitation training programs for physicians and other rehabilitation professionals. Patients had to pay out of pocket for these services in most of surveyed countries, followed by public funding and private insurance. Tele-neurorehabilitation and local disease-specific neurorehabilitation guidelines were not available in most of surveyed countries. Conclusion: Neurorehabilitation services in OIC region are not widely available, with significant variability in availability and quality. There is a need to develop and improve these services. Sharing knowledge and expertise through various forms, such as exchange visits and online sessions, can help improve neurorehabilitation services in these countries

Variable Handle-Resistance Based Joystick for Post-stroke Neurorehabilitation Training of Hand and Wrist in Upper Extremities

Variable Handle-Resistance Based Joystick for Post-stroke Neurorehabilitation Training of Hand and Wrist in Upper Extremities

EEG Brainwave Controlled Robotic Arm for Neurorehabilitation Training

This study presents an EEG-based control method using AI for a robotic arm neurorehabilitation system. The system employs advanced EEG technology to capture and interpret brainwaves, utilizing AI algorithms for analysis and translation. This enables users to control the robotic arm via neural signals during neurorehabilitation training. Real-time feedback and adaptive AI ensure a personalized, interactive experience, and adjust the program based on user progress. This approach aims to enhance neurorehabilitation outcomes by promoting neuroplasticity and motor skill recovery in individuals with neurological disorders or injuries. The study’s results highlight AI’s positive impact on the system, suggesting EEG-based AI control could play a pivotal role in neurorehabilitation.

Robotic arm training in neurorehabilitation enhanced by augmented reality – a usability and feasibility study

BackgroundRobotic therapy and serious gaming support motor learning in neurorehabilitation. Traditional monitor-based gaming outputs cannot adequately represent the third dimension, whereas virtual reality headsets lack the connection to the real world. The use of Augmented Reality (AR) techniques could potentially overcome these issues. The objective of this study was thus to evaluate the usability, feasibility and functionality of a novel arm rehabilitation device for neurorehabilitation (RobExReha system) based on a robotic arm (LBR iiwa, KUKA AG) and serious gaming using the AR headset HoloLens (Microsoft Inc.).MethodsThe RobExReha system was tested with eleven adult inpatients (mean age: 64.4 ± 11.2 years; diagnoses: 8 stroke, 2 spinal cord injury, 1 Guillain-Barré-Syndrome) who had paretic impairments in their upper limb. Five therapists administered and evaluated the system. Data was compared with a Reference Group (eleven inpatients; mean age: 64.3 ± 9.1 years; diagnoses: 10 stroke, 1 spinal cord injury) who trained with commercially available robotic therapy devices (ArmeoPower or ArmeoSpring, Hocoma AG). Patients used standardized questionnaires for evaluating usability and comfort (Quebec User Evaluation of Satisfaction with assistive technology [QUEST]), workload (Raw Task Load Index [RTLX]) and a questionnaire for rating visual perception of the gaming scenario. Therapists used the QUEST, the System Usability Scale and the short version of the User Experience Questionnaire.ResultsTherapy with the RobExReha system was safe and feasible for patients and therapists, with no serious adverse events being reported. Patients and therapists were generally satisfied with usability. The patients’ usability ratings were significantly higher in the Reference Group for two items of the QUEST: reliability and ease of use. Workload (RTLX) ratings did not differ significantly between the groups. Nearly all patients using the RobExReha system perceived the gaming scenario in AR as functioning adequately despite eight patients having impairments in stereoscopic vision. The therapists valued the system’s approach as interesting and inventive.ConclusionsWe demonstrated the clinical feasibility of combining a novel robotic upper limb robot with an AR-serious game in a neurorehabilitation setting. To ensure high usability in future applications, a reliable and easy-to-use system that can be used for task-oriented training should be implemented.Trial registrationEthical approval was obtained and the trial was registered at the German Clinical Trials Register (DRKS00022136).

Spanish consensus of occupational therapists on upper limb assessment tools in stroke

Introduction: There is a lack of explicit tool recommendations for upper limb (UL) assessment in stroke, occupational therapists are frequently underrepresented in consensus studies, and the frequency of use of tools is highly variable between countries. The objective was to generate national occupational therapy consensus recommendations on UL assessment tools in stroke, and to classify the tools that achieve consensus according to the International Classification of Functioning, Disability and Health (ICF) components. Methods: Three-round e-Delphi study of national scope. Occupational therapists working in Spain with training and experience in neurorehabilitation were recruited. Rounds were based on the completion of questionnaires on UL stroke assessment tools. Consensus was reached when ⩾75% of experts gave a ⩾7 rating on a nine-point Likert scale. Results: A total of 29 occupational therapists comprised the expert panel. Twenty-three (17.8%) assessment tools achieved consensus and were classified according to the ICF components of body functions and structures (7), activities (11), participation (3) and other (2). Conclusion: The expert panel recommended 23 outcome measures for UL recovery in stroke, representing all ICF components. The consensus recommendations are intended to assist occupational therapists in their clinical decision-making process, and to reduce the heterogeneity of research tools.

The Effect of Diagonal Exercise Training for Neurorehabilitation on Functional Activity in Stroke Patients: A Pilot Study.

Functional movements of the human body occur multifacetedly. This pilot study investigated the effects of neurorehabilitation training, including diagonal movements, balance, gait, fall efficacy, and activities of daily living in stroke patients. Twenty-eight patients diagnosed with stroke by a specialist were divided into experimental groups applying diagonal exercise training and control groups applying sagittal exercise training. The five times sit-to-stand test (FTSST), timed up and go (TUG) test, and Berg balance scale (BBS) were used to evaluate balance ability, the falls efficacy scale (FES) was used to evaluate fall efficacy, and the modified Barthel index (MBI) was used to evaluate activities of daily living. All evaluations were conducted once prior to intervention implementation and again six weeks after the final intervention. In the study results, the experimental group to which the diagonal exercise training was applied had statistically significant changes in FTSST, BBS, and FES compared to the control group. In conclusion, the rehabilitation program, including diagonal exercise training, increased the patient's balance and reduced the fear of falling.

Neuromechanics-Based Neural Feedback Controller for Planar Arm Reaching Movements.

Based on the principles of neuromechanics, human arm movements result from the dynamic interaction between the nervous, muscular, and skeletal systems. To develop an effective neural feedback controller for neuro-rehabilitation training, it is important to consider both the effects of muscles and skeletons. In this study, we designed a neuromechanics-based neural feedback controller for arm reaching movements. To achieve this, we first constructed a musculoskeletal arm model based on the actual biomechanical structure of the human arm. Subsequently, a hybrid neural feedback controller was developed that mimics the multifunctional areas of the human arm. The performance of this controller was then validated through numerical simulation experiments. The simulation results demonstrated a bell-shaped movement trajectory, consistent with the natural motion of human arm movements. Furthermore, the experiment testing the tracking ability of the controller revealed real-time errors within one millimeter, with the tensile force generated by the controller's muscles being stable and maintained at a low value, thereby avoiding the issue of muscle strain that can occur due to excessive excitation during the neurorehabilitation process.

Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition

Joint attention skills deficiency in Autism spectrum disorder (ASD) hinders individuals from communicating effectively. The P300 Electroencephalogram (EEG) signal-based brain-computer interface (BCI) helps these individuals in neurorehabilitation training to overcome this deficiency. The detection of the P300 signal is more challenging in ASD as it is noisy, has less amplitude, and has a higher latency than in other individuals. This paper presents a novel application of the variational mode decomposition (VMD) technique in a BCI system involving ASD subjects for P300 signal identification. The EEG signal is decomposed into five modes using VMD. Thirty linear and non-linear time and frequency domain features are extracted for each mode. Synthetic minority oversampling technique data augmentation is performed to overcome the class imbalance problem in the chosen dataset. Then, a comparative analysis of three popular machine learning classifiers is performed for this application. VMD's fifth mode with a support vector machine (fine Gaussian kernel) classifier gave the best performance parameters, namely accuracy, F1-score, and the area under the curve, as 91.12%, 91.18%, and 96.6%, respectively. These results are better when compared to other state-of-the-art methods.

Synchronous analyses between electroencephalogram and surface electromyogram based on motor imagery and motor execution.

The functional coupling of the cerebral cortex and muscle contraction indicates that electroencephalogram (EEG) and surface electromyogram (sEMG) signals are coherent. The objective of this study is to clearly describe the coupling relationship between EEG and sEMG through a variety of analysis methods. We collected the EEG and sEMG data of left- or right-hand motor imagery and motor execution from six healthy subjects and six stroke patients. To enhance the coherence coefficient between EEG and sEMG signals, the algorithm of EEG modification based on the peak position of sEMG signals is proposed. Through analyzing a variety of signal synchronization analysis methods, the most suitable coherence analysis algorithm is selected. In addition, the wavelet coherence analysis method based on time spectrum estimation was used to study the linear correlation characteristics of the frequency domain components of EEG and sEMG signals, which verified that wavelet coherence analysis can effectively describe the temporal variation characteristics of EEG-sEMG coherence. In the task of motor imagery, the significant EEG-sEMG coherence is mainly in the imagination process with the frequency distribution of the alpha and beta frequency bands; in the task of motor execution, the significant EEG-sEMG coherence mainly concentrates before and during the task with the frequency distribution of the alpha, beta, and gamma frequency bands. The results of this study may provide a theoretical basis for the cooperative working mode of neurorehabilitation training and introduce a new method for evaluating the functional state of neural rehabilitation movement.

Use of Virtual Reality in Children with Dyslexia.

In recent years, the study of dyslexia has seen rapid progress in definition and classification, neuropsychological correlates, neurobiological factors, and intervention. However, there are few studies on how virtual reality can affect improving cognitive domains and cross-cutting pedagogical skills. We, therefore, tested intervention through the use of a virtual reality rehabilitation system (VRRS) in children with dyslexia. Twenty-eight patients diagnosed with dyslexia were enrolled in this study. One-half underwent conventional neuropsychological treatment, and the other half performed VR neurorehabilitation training using the VRRS. All patients were evaluated by neuropsychological assessment at baseline (T0) and at the end of the protocol (T1). The assessment included the administration of the Wechsler Intelligence Scale for Children-IV and the Italian Battery for the Assessment of Dyslexia and Dysorthography. Our results showed a significant difference in word-reading test scores as well as in homophonic writing. In addition, treatment type was found to affect some domains of the WISC. We believe that the VRRS led to improved outcomes through the use of VR, which encourages active exploration, improves engagement, and provides motivation and enjoyment, allowing longer training sessions and improving treatment adherence.

Identification of a novel pathogenic TBCK variant in a Chinese patient with infantile hypotonia with psychomotor retardation and characteristic facies type 3 (IHPRF3): a case report

BackgroundInfantile hypotonia with psychomotor retardation and characteristic facies type 3(IHPRF3) (OMIM #616,900) is an autosomal recessive disorder caused by biallelic pathogenic variants of the TBCK gene, and to date, this disease was reported rather limitedly in number and all described cases were Caucasians.Case presentationThis paper reported the clinical and genetic features of a Chinese patient with IHPRF3. The patient was a 15-month-old male with global developmental delay, profound hypotonia, and typical facial dysmorphic features including mildly coarse facial appearance, hypertelorism, tented upper lip, exaggerated Cupid’s bow, macroglossia and arched eyebrows. Magnetic Resonance Imaging (MRI) analysis of the brain revealed slightly widened bilateral ventricles and subarachnoid space. On genetic analysis, the patient was homozygous for a novel TBCK variant c.247C > T(p.Arg83Ter). The parents were both carriers without any positive symptoms or signs. With an extremely low frequency (0.0000082) in Exome Aggregation Consortium, the variant has not been reported in any other databases or official literatures, and was diagnosed to be pathogenic according to the American College of Medical Genetics and Genomics(ACMG) standards and guidelines. Neurorehabilitation training did not work well and the long-term prognosis remained to be observed.ConclusionsThis study reported the clinical and molecular features of the first non-Caucasian patient with IHPRF3 arising from a novel homozygous TBCK mutation, which provided a novel molecular marker for the definite diagnosis of IHPRF3 patients and for its genetic counseling and prenatal diagnosis in the affected families.

Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study.

Brain-computer interfaces (BCI) that enables people with severe motor disabilities to use their brain signals for direct control of objects have attracted increased interest in rehabilitation. To date, no study has investigated feasibility of the BCI framework incorporating both intracortical and scalp signals. Concurrent local field potential (LFP) from the hand-knob area and scalp EEG were recorded in a paraplegic patient undergoing a spike-based close-loop neurorehabilitation training. Based upon multimodal spatio-spectral feature extraction and Naïve Bayes classification, we developed, for the first time, a novel LFP-EEG-BCI for motor intention decoding. A transfer learning (TL) approach was employed to further improve the feasibility. The performance of the proposed LFP-EEG-BCI for four-class upper-limb motor intention decoding was assessed. Using a decision fusion strategy, we showed that the LFP-EEG-BCI significantly (p 0.05) outperformed single modal BCI (LFP-BCI and EEG-BCI) in terms of decoding accuracy with the best performance achieved using regularized common spatial pattern features. Interrogation of feature characteristics revealed discriminative spatial and spectral patterns, which may lead to new insights for better understanding of brain dynamics during different motor imagery tasks and promote development of efficient decoding algorithms. Moreover, we showed that similar classification performance could be obtained with few training trials, therefore highlighting the efficacy of TL. The present findings demonstrated the superiority of the novel LFP-EEG-BCI in motor intention decoding. This work introduced a novel LFP-EEG-BCI that may lead to new directions for developing practical neurorehabilitation systems with high detection accuracy and multi-paradigm feasibility in clinical applications.

"Tricking the Brain" Using Immersive Virtual Reality: Modifying the Self-Perception Over Embodied Avatar Influences Motor Cortical Excitability and Action Initiation.

To offer engaging neurorehabilitation training to neurologic patients, motor tasks are often visualized in virtual reality (VR). Recently introduced head-mounted displays (HMDs) allow to realistically mimic the body of the user from a first-person perspective (i.e., avatar) in a highly immersive VR environment. In this immersive environment, users may embody avatars with different body characteristics. Importantly, body characteristics impact how people perform actions. Therefore, alternating body perceptions using immersive VR may be a powerful tool to promote motor activity in neurologic patients. However, the ability of the brain to adapt motor commands based on a perceived modified reality has not yet been fully explored. To fill this gap, we “tricked the brain” using immersive VR and investigated if multisensory feedback modulating the physical properties of an embodied avatar influences motor brain networks and control. Ten healthy participants were immersed in a virtual environment using an HMD, where they saw an avatar from first-person perspective. We slowly transformed the surface of the avatar (i.e., the “skin material”) from human to stone. We enforced this visual change by repetitively touching the real arm of the participant and the arm of the avatar with a (virtual) hammer, while progressively replacing the sound of the hammer against skin with stone hitting sound via loudspeaker. We applied single-pulse transcranial magnetic simulation (TMS) to evaluate changes in motor cortical excitability associated with the illusion. Further, to investigate if the “stone illusion” affected motor control, participants performed a reaching task with the human and stone avatar. Questionnaires assessed the subjectively reported strength of embodiment and illusion. Our results show that participants experienced the “stone arm illusion.” Particularly, they rated their arm as heavier, colder, stiffer, and more insensitive when immersed with the stone than human avatar, without the illusion affecting their experienced feeling of body ownership. Further, the reported illusion strength was associated with enhanced motor cortical excitability and faster movement initiations, indicating that participants may have physically mirrored and compensated for the embodied body characteristics of the stone avatar. Together, immersive VR has the potential to influence motor brain networks by subtly modifying the perception of reality, opening new perspectives for the motor recovery of patients.

Chapter 23 - Reward and plasticity: Implications for neurorehabilitation

Neuroplasticity follows nervous system injury in the presence or absence of rehabilitative treatments. Rehabilitative interventions can be used to modulate adaptive neuroplasticity, reducing motor impairment and improving activities of daily living in patients with brain lesions. Learning principles guide some rehabilitative interventions. While basic science research has shown that reward combined with training enhances learning, this principle has been only recently explored in the context of neurorehabilitation. Commonly used reinforcers may be more or less rewarding depending on the individual or the context in which the task is performed. Studies in healthy humans showed that both reward and punishment can enhance within-session motor performance; but reward, and not punishment, improves consolidation and retention of motor skills. On the other hand, neurorehabilitative training after brain lesions involves complex tasks (e.g., walking and activities of daily living). The contribution of reward to neurorehabilitation is incompletely understood. Here, we discuss recent research on the role of reward in neurorehabilitation and the needed directions of future research.

Abstract P206: Wearable Myoelectric Interface Training for Improving Arm Movement in Chronic Stroke

Impaired arm movement after stroke is due not only to weakness and spasticity, but also to abnormal co-activation (a.k.a. abnormal synergies). Yet, few stroke therapies are aimed to reduce this co-activation. We designed a myoelectric computer interface (MyoCI) that provides feedback to stroke survivors to reduce this abnormal co-activation and associated arm impairment. Here, we describe a novel, wearable version of the MyoCI to control custom-designed computer games as a home-based therapy for chronic stroke survivors. This therapy, called the myoelectric interface for neurorehabilitation training (MINT), enables high-intensity, high-dose training using an inexpensive device at home. The MINT device records surface EMGs from 2-3 arm muscles and wirelessly controls games on a laptop or tablet. By mapping the muscle activities to orthogonal directions of cursor movements, the games operantly condition the participants to learn to reduce abnormal co-activation between the muscles. We are currently investigating this device in severely impaired stroke survivors (Fugl-Meyer score <30). Participants were asked to train 90 min per day, 6 days a week, for a total of 6 weeks, with different variants of the paradigm (2 or 3 muscles at a time) vs. a sham control group that trained on one muscle. We surveyed participants about their engagement with the game using a modified Intrinsic Motivation Inventory (IMI). Eighteen participants have completed the 6-week training to date. They averaged 75±19 min of daily training out of the 90 that were asked. Overall, 60% of participants surveyed enjoyed, and 90% were positively motivated by, the training. A few participants had significant difficulty with the laptops due to inexperience with computers. Participants’ performance on the game, measured by weighted time-to-target, improved steadily overall in the experimental group, while the sham control group stayed at high performance throughout. The MINT provides a motivating, enjoyable way to provide high-intensity therapy in the home to stroke survivors. This therapy is a new mechanism of action for stroke rehabilitation and we are evaluating its efficacy in an ongoing randomized controlled trial.

A Comparison of Non-negative Tucker Decomposition and Parallel Factor Analysis for Identification and Measurement of Human EEG Rhythms

Abstract Analysis of changes in the brain neural electrical activity measured by the electroencephalogram (EEG) plays a crucial role in the area of brain disorder diagnostics. The elementary latent sources of the brain neural activity can be extracted by a tensor decomposition of continuously recorded multichannel EEG. Parallel factor analysis (PARAFAC) is a powerful approach for this purpose. However, the assumption of the same number of factors in each dimension of the PARAFAC model may be restrictive when applied to EEG data. In this article we discuss the potential benefits of an alternative tensor decomposition method – the Tucker model. We analyze situations, where in comparison to the PARAFAC solution, the Tucker model provides a more parsimonious representation of the EEG data decomposition. We show that this more parsimonious representation of EEG is achieved without reducing the ability to explain variance. We analyze EEG records of two patients after ischemic stroke and we focus on the extraction of specific sensorimotor oscillatory sources associated with motor imagery during neurorehabilitation training. Both models provided consistent results. The advantage of the Tucker model was a compact structure with only two spatial signatures reflecting the expected lateralized activation of the detected subject-specific sensorimotor rhythms.

Look at the cognitive deficits in patients with myotonic dystrophy type 1: an exploratory research on the effects of virtual reality.

Myotonic dystrophy type 1 is a neuromuscular disease, characterized by a progressive loss of strength, muscle stiffness, and difficulty in relaxation. Myotonic dystrophy type 1 patients can present several neuropsychological deficits, as well as anxiety and mood disorders. Aim of this study is to evaluate the feasibility and the effect of virtual reality in the cognitive and behavioral recovery of myotonic dystrophy type 1 patients. Eleven patients (8 female and 3 male) underwent a specific cognitive rehabilitation program including a conventional neuropsychological treatment followed by a virtual reality neurorehabilitation training using the Virtual Reality Rehabilitation System (Khymeia, Italy). Virtual reality improved many cognitive domains, including executive function, attention, verbal and visuo-spatial abilities, as well as mood and coping strategies. Due to the high prevalence of neuropsychological symptoms in patients with myotonic dystrophy type 1, cognitive rehabilitation should enter into the framework of these patients to potentially boost cognitive and behavioral function and improve quality of life.

Peer review (Geert Verheyden) - Ten guiding principles for movement training in neurorehabilitation

Peer review (Geert Verheyden) - Ten guiding principles for movement training in neurorehabilitation

Peer review (Ken Chance-Larsen) - Ten guiding principles for movement training in neurorehabilitation

Peer review (Ken Chance-Larsen) - Ten guiding principles for movement training in neurorehabilitation