Robotic Hand Orthosis Research Articles

Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis

Purpose Wearable robotic devices are currently being developed to improve upper limb function for individuals with hemiparesis after stroke. Incorporating the views of clinicians during the development of new technologies can help ensure that end products meet clinical needs and can be adopted for patient care. Methods In this cross-sectional mixed-methods study, an anonymous online survey was used to gather clinicians’ perceptions of a wearable robotic hand orthosis for post-stroke hemiparesis. Participants were asked about their clinical experience and provided feedback on the prototype device after viewing a video. Results 154 participants completed the survey. Only 18.8% had previous experience with robotic technology. The majority of participants (64.9%) reported that they would use the device for both rehabilitative and assistive purposes. Participants perceived that the device could be used in supervised clinical settings with all phases of stroke. Participants also indicated a need for insurance coverage and quick setup time. Conclusions Engaging clinicians early in the design process can help guide the development of wearable robotic devices. Both rehabilitative and assistive functions are valued by clinicians and should be considered during device development. Future research is needed to understand a broader set of stakeholders’ perspectives on utility and design.

Mixed methods usability evaluation of an assistive wearable robotic hand orthosis for people with spinal cord injury

BackgroundRobotic hand orthoses (RHO) aim to provide grasp assistance for people with sensorimotor hand impairment during daily tasks. Many of such devices have been shown to bring a functional benefit to the user. However, assessing functional benefit is not sufficient to evaluate the usability of such technologies for daily life application. A comprehensive and structured evaluation of device usability not only focusing on effectiveness but also efficiency and satisfaction is required, yet often falls short in existing literature. Mixed methods evaluations, i.e., assessing a combination of quantitative and qualitative measures, allow to obtain a more holistic picture of all relevant aspects of device usability. Considering these aspects already in early development stages allows to identify design issues and generate generalizable benchmarks for future developments.MethodsWe evaluated the short-term usability of the RELab tenoexo, a RHO for hand function assistance, in 15 users with tetraplegia after a spinal cord injury through a comprehensive mixed methods approach. We collected quantitative data using the Action Research Arm Test (ARAT), the System Usability Scale (SUS), and timed tasks such as the donning process. In addition, qualitative data were collected through semi-structured interviews and user observations, and analyzed with a thematic analysis to enhance the usability evaluation. All insights were attributed and discussed in relation to specifically defined usability attributes such as comfort, ease of use, functional benefit, and safety.ResultsThe RELab tenoexo provided an immediate functional benefit to the users, resulting in a mean improvement of the ARAT score by 5.8 points and peaking at 15 points improvement for one user (clinically important difference: 5.7 points). The mean SUS rating of 60.6 represents an adequate usability, however, indicating that especially the RHO donning (average task time = 295 s) was perceived as too long and cumbersome. The participants were generally very satisfied with the ergonomics (size, dimensions, fit) of the RHO. Enhancing the ease of use, specifically in donning, increasing the provided grasping force, as well as the availability of tailoring options and customization were identified as main improvement areas to promote RHO usability.ConclusionThe short-term usability of the RELab tenoexo was thoroughly evaluated with a mixed methods approach, which generated valuable data to improve the RHO in future iterations. In addition, learnings that might be transferable to the evaluation and design of other RHO were generated, which have the potential to increase the daily life applicability and acceptance of similar technologies.

A Tailorable Robotic Hand Orthosis to Support Children with Neurological Hand Impairments: a Case Study in a Child's Home.

Neurological disorders such as traumatic brain injuries (TBI) can lead to hand impairments in children, negatively impacting their quality of life. Fully wearable robotic hand orthoses (RHO) have been proposed to actively support children and promote the use of the impaired limb in daily life. Here we report a case study on the feasibility of using the pediatric RHO PEXO for assistance at home in a 13- year-old child with hand impairment after TBI. The size and functionalities of the RHO were first fully tailored to the child's needs. We trained the child and their parent on independently using the RHO before taking it home for a period of two weeks. The use of the RHO improved hand ability. Additionally, the tailoring and training benefited the unimanual capacity (Box and Block Test score +2 after tailoring) and bimanual performance (Assisting Hand Assessment score +4) of the child with PEXO. Further, it increased device acceptance by the child and the parent. The child used PEXO at home for 76 minutes distributed over three days during eating and drinking tasks. Personal and environmental factors caused the moderate use. No adverse events or safety-related issues occurred. This study highlights the value of tailoring an assistive RHO and, for the first time, demonstrates the feasibility of home use of a pediatric RHO by children with neurological hand impairments.

Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals

BackgroundAssistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated.MethodsIn this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance.ResultsWe achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4 up to 100%. Averaged inference times per sample were measured below 0.15 ms. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance.ConclusionThe results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a robotic hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living.

Development of a robotic orthosis for fingers flexion motion by surface myoelectric control: Open source prototype

Objective:This study proposed an open-source myoelectric robotic hand orthosis prototype to support people with neuromuscular disorders in performing activities of daily living (ADLs). Methods:A user-tuned methodology was applied, designing the system’s usability and ergonomics with recommendations in literature (2017 to 2020), thus calibrating the prototype by user’s metrics. It has two modules: transmitter–interpreter system (TIS) and receiver–actuator system (RAS) that performs three hand pose: (i) pulp pinch, (ii) cylindrical grip, and (iii) resting hand. The system uses a linear discriminant analysis (LDA) classifier with real-time disruptive windowing (80 ms) and a majority vote algorithm (n=3) as post-processing. TIS classifies and transmits a hand pose within 300 ms to RAS, which executes the hand pose. Results:The LDA classifier was trained with ten datasets of surface myoelectric signals (sMES) recorded from a healthy volunteer. The system’s accuracy reached 90% in real-time tests with three everyday objects. Splints were 3D-printed with resistant material, so the fitting and range of motion were comfortable for the volunteer. Conclusion:The prototype achieved the recommendations in the literature, and it was the orthosis that most fulfilled said requirements compared to state-of-the-art (2017 to 2020) myoelectric robotic hand orthosis. However, it only assists with finger flexion. Significance:The open-source format provides other researchers a starting point to develop an orthosis for in-home rehabilitation that actively assists in ADLs. Additionally, production costs for the orthosis are R$856.35, or 2.98% of the cheapest myoelectric hand orthosis available on the market (PowerGrip), both quoted on November 2021.

Continuous versus discrete robotic feedback for brain-computer interfaces aimed for neurorehabilitation.

Brain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task. An assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with each feedback. Significantly higher BCI performance (65.4 ± 17.9% with the continuous and 62.1 ± 18.6% with the discrete feedback) and pronounced bilateral alpha and ipsilateral beta cortical activations were observed with the continuous feedback. It was hypothesized that these effects, although heterogenous across participants, were caused by the enhancement of attentional and closed-loop somatosensory processes. This is important, since a continuous feedback timing could increase the number of BCI users that can control a MI-based system or enhance cortical activations associated with neuroplasticity, important for neurorehabilitation applications.

Individuals with moderate to severe hand impairments may struggle to use EMG control for assistive devices.

Neurological trauma, such as stroke, traumatic brain injury (TBI), spinal cord injury, and cerebral palsy can cause mild to severe upper limb impairments. Hand impairment makes it difficult for individuals to complete activities of daily living, especially bimanual tasks. A robotic hand orthosis or hand exoskeleton can be used to restore partial function of an intact but impaired hand. It is common for upper extremity prostheses and orthoses to use electromyography (EMG) sensing as a method for the user to control their device. However some individuals with an intact but impaired hand may struggle to use a myoelectrically controlled device due to potentially confounding muscle activity. This study was conducted to evaluate the application of conventional EMG control techniques as a robotic orthosis/exoskeleton user input method for individuals with mild to severe hand impairments. Nine impaired subjects and ten healthy subjects were asked to perform repeated contractions of muscles in their forearm and then onset analysis and feature classification were used to determine the accuracy of the employed EMG techniques. The average accuracy for contraction identification across employed EMG techniques was 95.4% ± 4.9 for the healthy subjects and 73.9% ± 13.1 for the impaired subjects with a range of 47.0% ± 19.1 - 91.6% ± 8.5. These preliminary results suggest that the conventional EMG control technologies employed in this paper may be difficult for some impaired individuals to use due to their unreliable muscle control.

Thumb Stabilization and Assistance in a Robotic Hand Orthosis for Post-Stroke Hemiparesis.

We propose a dual-cable method of stabilizing the thumb in the context of a hand orthosis designed for individuals with upper extremity hemiparesis after stroke. This cable network adds opposition/reposition capabilities to the thumb, and increases the likelihood of forming a hand pose that can successfully manipulate objects. In addition to a passive-thumb version (where both cables are of fixed length), our approach also allows for a single-actuator active-thumb version (where the extension cable is actuated while the abductor remains passive), which allows a range of motion intended to facilitate creating and maintaining grasps. We performed experiments with five chronic stroke survivors consisting of unimanual resistive-pull tasks and bimanual twisting tasks with simulated real-world objects; these explored the effects of thumb assistance on grasp stability and functional range of motion. Our results show that both active- and passive-thumb versions achieved similar performance in terms of improving grasp force generation over a no-device baseline, but active thumb stabilization enabled users to maintain grasps for longer durations.

Adaptive Semi-Supervised Intent Inferral to Control a Powered Hand Orthosis for Stroke.

In order to provide therapy in a functional context, controls for wearable robotic orthoses need to be robust and intuitive. We have previously introduced an intuitive, user-driven, EMG-based method to operate a robotic hand orthosis, but the process of training a control that is robust to concept drift (changes in the input signal) places a substantial burden on the user. In this paper, we explore semi-supervised learning as a paradigm for controlling a powered hand orthosis for stroke subjects. To the best of our knowledge, this is the first use of semi-supervised learning for an orthotic application. Specifically, we propose a disagreement-based semi-supervision algorithm for handling intrasession concept drift based on multimodal ipsilateral sensing. We evaluate the performance of our algorithm on data collected from five stroke subjects. Our results show that the proposed algorithm helps the device adapt to intrasession drift using unlabeled data and reduces the training burden placed on the user. We also validate the feasibility of our proposed algorithm with a functional task; in these experiments, two subjects successfully completed multiple instances of a pick-and-handover task.

Theta-gamma coupling as a cortical biomarker of brain-computer interface-mediated motor recovery in chronic stroke.

Chronic stroke patients with upper-limb motor disabilities are now beginning to see treatment options that were not previously available. To date, the two options recently approved by the United States Food and Drug Administration include vagus nerve stimulation and brain–computer interface therapy. While the mechanisms for vagus nerve stimulation have been well defined, the mechanisms underlying brain–computer interface-driven motor rehabilitation are largely unknown. Given that cross-frequency coupling has been associated with a wide variety of higher-order functions involved in learning and memory, we hypothesized this rhythm-specific mechanism would correlate with the functional improvements effected by a brain–computer interface. This study investigated whether the motor improvements in chronic stroke patients induced with a brain–computer interface therapy are associated with alterations in phase–amplitude coupling, a type of cross-frequency coupling. Seventeen chronic hemiparetic stroke patients used a robotic hand orthosis controlled with contralesional motor cortical signals measured with EEG. Patients regularly performed a therapeutic brain–computer interface task for 12 weeks. Resting-state EEG recordings and motor function data were acquired before initiating brain–computer interface therapy and once every 4 weeks after the therapy. Changes in phase–amplitude coupling values were assessed and correlated with motor function improvements. To establish whether coupling between two different frequency bands was more functionally important than either of those rhythms alone, we calculated power spectra as well. We found that theta–gamma coupling was enhanced bilaterally at the motor areas and showed significant correlations across brain–computer interface therapy sessions. Importantly, an increase in theta–gamma coupling positively correlated with motor recovery over the course of rehabilitation. The sources of theta–gamma coupling increase following brain–computer interface therapy were mostly located in the hand regions of the primary motor cortex on the left and right cerebral hemispheres. Beta–gamma coupling decreased bilaterally at the frontal areas following the therapy, but these effects did not correlate with motor recovery. Alpha–gamma coupling was not altered by brain–computer interface therapy. Power spectra did not change significantly over the course of the brain–computer interface therapy. The significant functional improvement in chronic stroke patients induced by brain–computer interface therapy was strongly correlated with increased theta–gamma coupling in bihemispheric motor regions. These findings support the notion that specific cross-frequency coupling dynamics in the brain likely play a mechanistic role in mediating motor recovery in the chronic phase of stroke recovery.

Brain-Computer Interface Coupled to a Robotic Hand Orthosis for Stroke Patients' Neurorehabilitation: A Crossover Feasibility Study.

Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the rehabilitation of stroke patients. However, little has been reported that compares the clinical and physiological effects of a BCI intervention for upper limb stroke rehabilitation with those of conventional therapy. This study assesses the feasibility of an intervention with a BCI based on electroencephalography (EEG) coupled to a robotic hand orthosis for upper limb stroke rehabilitation and compares its outcomes to conventional therapy. Seven subacute and three chronic stroke patients (M = 59.9 ± 12.8) with severe upper limb impairment were recruited in a crossover feasibility study to receive 1 month of BCI therapy and 1 month of conventional therapy in random order. The outcome measures were comprised of: Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), motor evoked potentials elicited by transcranial magnetic stimulation (TMS), hand dynamometry, and EEG. Additionally, BCI performance and user experience were measured. All measurements were acquired before and after each intervention. FMA-UE and ARAT after BCI (23.1 ± 16; 8.4 ± 10) and after conventional therapy (21.9 ± 15; 8.7 ± 11) were significantly higher (p < 0.017) compared to baseline (17.5 ± 15; 4.3 ± 6) but were similar between therapies (p > 0.017). Via TMS, corticospinal tract integrity could be assessed in the affected hemisphere of three patients at baseline, in five after BCI, and four after conventional therapy. While no significant difference (p > 0.05) was found in patients’ affected hand strength, it was higher after the BCI therapy. EEG cortical activations were significantly higher over motor and non-motor regions after both therapies (p < 0.017). System performance increased across BCI sessions, from 54 (50, 70%) to 72% (56, 83%). Patients reported moderate mental workloads and excellent usability with the BCI. Outcome measurements implied that a BCI intervention using a robotic hand orthosis as feedback has the potential to elicit neuroplasticity-related mechanisms, similar to those observed during conventional therapy, even in a group of severely impaired stroke patients. Therefore, the proposed BCI system could be a suitable therapy option and will be further assessed in clinical trials.

Abstract P205: Rehabilitation With Brain Computer Interface to Improve Hand Function After Stroke

Background: Stroke is the third cause of disability and the first cause of motor impairment of the upper limb, which significantly reduces independence in activities of daily living. Therefore, the development and use of new therapeutic approaches is needed. Brain-computer interfaces (BCI) based on movement intention (MI) have the potential to improve hand function and brain plasticity after stroke. Purpose: Improve hand function after stroke with BCI with MI. Methods: 8 patients with subacute stroke, left or right hemiparesis was divided in two groups. The first received conventional therapy followed by BCI-MI therapy and the second was given BCI-MI therapy and then conventional therapy. The outcome variables were sensory-motor recovery, hand function, spasticity, level of disability, grip strength, change in motor threshold and amplitude of motor evoked potentials (MEP) and changes in desynchronization/synchronization event-related (EDR/EDS). Results: 4 patients were female, the average age 56 ± 12.7 years, and 87% were ischemic stroke. No differences were found between post-conventional therapy and postBCI-MI in the sensorimotor recovery, the spasticity, and the degree of deficiency in the strength. Only in hand function measured with the Action Research Arm Test (ARAT), a statistically significant difference was found in the BCI-MI therapy group (p= 0.0247). Dynamometry grip strength had a tendency to increase the Post-BCI group. A MEP was obtained in the ipsilesional primary motor cortex of 3 patients. A higher EDR was found in the injured hemisphere in favor of the post-BCI group (p= 0.0165). Conclusions: This study shows that BCI-MI rehabilitation, which combines MI with a robotic hand orthosis as feedback, has a significant improvement in hand function and a greater ERD in the ipsilesional primary motor cortex. It is necessary to carry out controlled clinical trials with a larger sample and with a greater frequency and duration of intervention to measure hand motor and functional improvement of patients after stroke.

Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control.

This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.

Development of a Robotic Hand Orthosis for Stroke Patient Rehabilitation

&lt;p class="0abstractCxSpFirst"&gt;This study developed a robotic orthosis capable of detecting the intention of a wearer to move their fingers, thereafter augmenting their muscle force. This was with the aim of producing a device that can be used in post-stroke hand rehabilitation. The design of the orthosis was based on an existing design, which was modified using BLENDER release 2.78 and printed with ABS plastic. An actuator was mounted at the rear end of the orthosis, to provide actuation to perform full range flexion and extension motion for digits. Force sensors were embedded at the fingertips of the orthosis to detect minute finger movements. For severe cases where stroke survivors are incapable of little finger movements, the study employed a brain-computer interface to detect the intent to move. The robotic orthosis achieved an accuracy of 64.1% and 62% in detecting unclench and clench activities respectively and actuating the orthosis digits in response. The results revealed that the design presented here can help provide effective hand rehabilitation. The study concluded that the design incorporated with BCI systems is capable of performing hand rehabilitation in a clinical setting as it obtains some level of accuracy in detecting patient intent to move and actuating in response. This design is low cost, and hence will lessen the economic burden for stroke survivors in a poor-resource country.&lt;/p&gt;&lt;p class="0abstractCxSpLast"&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;

Comparison of Feature Vector Compositions to Enhance the Performance of NIRS-BCI-Triggered Robotic Hand Orthosis for Post-Stroke Motor Recovery

Recently, brain–computer interfaces, combined with feedback systems and goal-oriented training, have been investigated for their capacity to promote functional recovery after stroke. Accordingly, we developed a brain–computer interface-triggered robotic hand orthosis that assists hand-closing and hand-opening for post-stroke patients without sufficient motor output. In this system, near-infrared spectroscopy is used to monitor the affected motor cortex, and a linear discriminant analysis-based binary classifier estimates hand posture. The estimated posture then wirelessly triggers the robotic hand orthosis. For better performance of the brain–computer interface, we tested feature windows of different lengths and varying feature vector compositions with motor execution data from seven neurologically intact participants. The interaction between a feature window and a delay in the hemodynamic response significantly affected both classification accuracy (Matthew Correlation Coefficient) and detection latency. The ‘preserving channels’ feature vector was able to increase accuracy by 13.14% and decrease latency by 29.48%, relative to averaging. Oxyhemoglobin combined with deoxyhemoglobin improved accuracy by 3.71% and decreased latency by 6.01% relative to oxyhemoglobin alone. Thus, the best classification performance resulted in an accuracy of 0.7154 and a latency of 2.8515 s. The hand rehabilitation system was successfully implemented using this feature vector composition, which yielded better classification performance.

Brain State-dependent Gain Modulation of Corticospinal Output in the Active Motor System

The communication through coherence hypothesis suggests that only coherently oscillating neuronal groups can interact effectively and predicts an intrinsic response modulation along the oscillatory rhythm. For the motor cortex (MC) at rest, the oscillatory cycle has been shown to determine the brain's responsiveness to external stimuli. For the active MC, however, the demonstration of such a phase-specific modulation of corticospinal excitability (CSE) along the rhythm cycle is still missing. Motor evoked potentials in response to transcranial magnetic stimulation (TMS) over the MC were used to probe the effect of cortical oscillations on CSE during several motor conditions. A brain-machine interface (BMI) with a robotic hand orthosis allowed investigating effects of cortical activity on CSE without the confounding effects of voluntary muscle activation. Only this BMI approach (and not active or passive hand opening alone) revealed a frequency- and phase-specific cortical modulation of CSE by sensorimotor beta-band activity that peaked once per oscillatory cycle and was independent of muscle activity. The active MC follows an intrinsic response modulation in accordance with the communication through coherence hypothesis. Furthermore, the BMI approach may facilitate and strengthen effective corticospinal communication in a therapeutic context, for example, when voluntary hand opening is no longer possible after stroke.

Multimodal Sensing and Interaction for a Robotic Hand Orthosis

Wearable robotic hand rehabilitation devices can allow greater freedom and flexibility than their workstation-like counterparts. However, the field is generally lacking effective methods by which the user can operate the device: such controls must be effective, intuitive, and robust to the wide range of possible impairment patterns. Even when focusing on a specific condition, such as stroke, the variety of encountered upper limb impairment patterns means that a single sensing modality, such as electromyography (EMG), might not be sufficient to enable controls for a broad range of users. To address this significant gap, we introduce a multimodal sensing and interaction paradigm for an active hand orthosis. In our proof-of-concept implementation, EMG is complemented by other sensing modalities, such as finger bend and contact pressure sensors. We propose multimodal interaction methods that utilize this sensory data as input, and show they can enable tasks for stroke survivors who exhibit different impairment patterns. We believe that robotic hand orthoses developed as multimodal sensory platforms with help address some of the key challenges in physical interaction with the user.

Robotic orthosis compared to virtual hand for Brain–Computer Interface feedback

Brain–Computer Interfaces (BCI) allow the control of external devices by decoding the users’ intentions from their central nervous system. Feedback, one of the main elements of a closed-loop BCI, is used to enhance the user's performance. The present work aimed to compare the effect of two different feedback sources; congruent anatomical visual hand representation and passive hand movement on BCI performance and cortical activations. Electroencephalography of 12 healthy right-handed subjects was recorded to set a BCI activated by right-hand motor imagery. Afterward, the subjects were asked to control the system by imagining the movement. The system provided either visual feedback, shown on a computer screen or kinesthetic feedback, provided by a robotic hand orthosis. Differences in performance and cortical activations were assessed, using classification accuracy and event-related desynchronization/synchronization in μ and β bands, respectively. Performance was significantly better with kinesthetic feedback as it allowed for higher correct classification of motor imagery. Cortical activations in the ipsilateral central channel in μ were different between the two feedback modalities. Our results imply that healthy subjects can achieve a greater degree of control using a motor imagery-based BCI with kinesthetic feedback than with anatomically congruent visual feedback. Furthermore, cortical activation differences show that kinesthetic feedback seems to elicit higher recruitment of sensorimotor cortex brain cells, which probably reflects enhanced local information modulation related to fine motor processing. Therefore, kinesthetic feedback provided by a robotic orthosis could be a more suitable feedback strategy for BCI systems designed for neuromodulation and neurorehabilitation.

Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients.

Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A novel processing stage was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection. Offline tests were performed for testing the proposed processing stage, and results were compared with those computed with common spatial patterns. Afterwards, online tests with healthy subjects were performed in which the orthosis was activated by the system. Stroke patients' average performance was 74.1 ± 11%. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance than the common spatial pattern method. Healthy subjects' average offline and online performances were of 76.2 ± 7.6% and 70 ± 6.7, respectively. For 3 out of 8 healthy subjects, the proposed method showed a statistically significant higher performance than the common spatial pattern method. System's performance showed that it has a potential to be used for hand rehabilitation of stroke patients.

Fully embedded myoelectric control for a wearable robotic hand orthosis.

To prevent learned non-use of the affected hand in chronic stroke survivors, rehabilitative training should be continued after discharge from the hospital. Robotic hand orthoses are a promising approach for home rehabilitation. When combined with intuitive control based on electromyography, the therapy outcome can be improved. However, such systems often require extensive cabling, experience in electrode placement and connection to external computers. This paper presents the framework for a stand-alone, fully wearable and real-time myoelectric intention detection system based on the Myo armband. The hard and software for real-time gesture classification were developed and combined with a routine to train and customize the classifier, leading to a unique ease of use. The system including training of the classifier can be set up within less than one minute. Results demonstrated that: (1) the proposed algorithm can classify five gestures with an accuracy of 98%, (2) the final system can online classify three gestures with an accuracy of 94.3% and, in a preliminary test, (3) classify three gestures from data acquired from mildly to severely impaired stroke survivors with an accuracy of over 78.8%. These results highlight the potential of the presented system for electromyography-based intention detection for stroke survivors and, with the integration of the system into a robotic hand orthosis, the potential for a wearable platform for all day robot-assisted home rehabilitation.