Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability

Neural Interface-Based Motor Neuroprosthesis in Poststroke Upper Limb Neurorehabilitation: An Individual Patient Data Meta-analysis

OBJECTIVE

To determine the efficacy of neural interface-based neurorehabilitation, including brain-computer interface, through conventional and individual patient data (IPD) meta-analysis and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation.

DATA SOURCES

PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed.

STUDY SELECTION

Studies using neural interface-controlled physical effectors (functional electrical stimulation and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper-extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed.

DATA EXTRACTION

Changes in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD.

DATA SYNTHESIS

Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE scores increased by a mean of 5.23 (95% confidence interval [CI]: 3.85-6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 vs ≤4 weeks. On IPD analysis, the mean time-to-improvement above minimal clinically important difference (MCID) was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41%-70%). Patients with severe impairment (P=.042) and age >50 years (P=.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (hazard ratio [HR] 0.15, P=.08 and HR 0.47, P=.06, respectively).

CONCLUSION

Neural interface-based motor rehabilitation resulted in significant, although modest, reductions in poststroke impairment and should be considered for wider applications in stroke neurorehabilitation.

Development and evaluation of a BCI-neurofeedback system with real-time EEG detection and electrical stimulation assistance during motor attempt for neurorehabilitation of children with cerebral palsy

In the realm of motor rehabilitation, Brain-Computer Interface Neurofeedback Training (BCI-NFT) emerges as a promising strategy. This aims to utilize an individual's brain activity to stimulate or assist movement, thereby strengthening sensorimotor pathways and promoting motor recovery. Employing various methodologies, BCI-NFT has been shown to be effective for enhancing motor function primarily of the upper limb in stroke, with very few studies reported in cerebral palsy (CP). Our main objective was to develop an electroencephalography (EEG)-based BCI-NFT system, employing an associative learning paradigm, to improve selective control of ankle dorsiflexion in CP and potentially other neurological populations. First, in a cohort of eight healthy volunteers, we successfully implemented a BCI-NFT system based on detection of slow movement-related cortical potentials (MRCP) from EEG generated by attempted dorsiflexion to simultaneously activate Neuromuscular Electrical Stimulation which assisted movement and served to enhance sensory feedback to the sensorimotor cortex. Participants also viewed a computer display that provided real-time visual feedback of ankle range of motion with an individualized target region displayed to encourage maximal effort. After evaluating several potential strategies, we employed a Long short-term memory (LSTM) neural network, a deep learning algorithm, to detect the motor intent prior to movement onset. We then evaluated the system in a 10-session ankle dorsiflexion training protocol on a child with CP. By employing transfer learning across sessions, we could significantly reduce the number of calibration trials from 50 to 20 without compromising detection accuracy, which was 80.8% on average. The participant was able to complete the required calibration trials and the 100 training trials per session for all 10 sessions and post-training demonstrated increased ankle dorsiflexion velocity, walking speed and step length. Based on exceptional system performance, feasibility and preliminary effectiveness in a child with CP, we are now pursuing a clinical trial in a larger cohort of children with CP.

The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: a meta-analysis and systematic review

PURPOSE

Many recent clinical studies have suggested that the combination of brain-computer interfaces (BCIs) can induce neurological recovery and improvement in motor function. In this review, we performed a systematic review and meta-analysis to evaluate the clinical effects of BCI-robot systems.

METHODS

The articles published from January 2010 to December 2020 have been searched by using the databases (EMBASE, PubMed, CINAHL, EBSCO, Web of Science and manual search). The single-group studies were qualitatively described, and only the controlled-trial studies were included for the meta-analysis. The mean difference (MD) of Fugl-Meyer Assessment (FMA) scores were pooled and the random-effects model method was used to perform the meta-analysis. The PRISMA criteria were followed in current review.

RESULTS

A total of 897 records were identified, eight single-group studies and 11 controlled-trial studies were included in our review. The systematic analysis indicated that the BCI-robot systems had a significant improvement on motor function recovery. The meta-analysis showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects (p > 0.05).

CONCLUSION

The use of BCI-robot systems has significant improvement on the motor function recovery of hemiparetic upper-limb, and there is a sustaining effect. The meta-analysis showed no statistical difference between the experimental group (BCI-robot) and the control group (robot). However, there are a few shortcomings in the experimental design of existing studies, more clinical trials need to be conducted, and the experimental design needs to be more rigorous.Implications for RehabilitationIn this review, we evaluated the clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation. After we screened the databases, 19 articles were included in this review. These articles all clinical trial research, they all used non-invasive brain-computer interfaces and upper-limb robot.We conducted the systematic review with nine articles, the result indicated that the BCI-robot system had a significant improvement on motor function recovery. Eleven articles were included for the meta-analysis, the result showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects.We thought the result of meta-analysis which showed no statistic difference was probably caused by the heterogenicity of clinical trial designs of these articles.We thought the BCI-robot systems are promising strategies for post-stroke rehabilitation. And we gave several suggestions for further research: (1) The experimental design should be more rigorous, and describe the experimental designs in detail, especially the control group intervention, to make the experiment replicability. (2) New evaluation criteria need to be established, more objective assessment such as biomechanical assessment, fMRI should be utilised as the primary outcome. (3) More clinical studies with larger sample size, novel external devices, and BCI systems need to be conducted to investigate the differences between BCI-robot system and other interventions. (4) Further research could shift the focus to the patients who are in subacute stage, to explore if the early BCI training can make a positive impact on cerebral cortical recovery.

Effects of acupuncture synchronized rehabilitation therapy on upper limb motor and sensory function after stroke: a study protocol for a single-center, 2 × 2 factorial design, randomized controlled trial

Background

Upper limb function reconstruction has been an important issue in the field of stroke rehabilitation. Due to the complexity of upper extremity dysfunction in stroke patients, the clinical efficacy produced by central or peripheral stimulation alone is limited. For this reason, our group has proposed acupuncture synchronized rehabilitation therapy (ASRT), i.e., simultaneous scalp acupuncture and intradermal acupuncture during rehabilitation. Pre-experiments results showed that this therapy can effectively improve the motor and sensory functions of upper limbs in post-stroke patients, but the clinical efficacy and safety of ASRT need to be further verified, and whether there is a synergistic effect between scalp acupuncture and intradermal acupuncture also needs to be studied in depth. Therefore, we designed a randomized controlled trial to compare the efficacy and safety of different therapies to explore a more scientific "synchronous treatment model."

Methods

This is a single-center, randomized controlled trial using a 2 × 2 factorial design. We will recruit 136 stroke survivors with upper extremity dysfunction and randomize them into four groups (n = 34). All subjects will undergo routine treatment, based on which the Experimental Group 1: rehabilitation training synchronized with intradermal acupuncture treatment of the affected upper limb; Experimental Group 2: rehabilitation training of the affected upper limb synchronized with focal-side scalp acupuncture treatment, and Experimental Group 3: rehabilitation training synchronized with intradermal acupuncture treatment of the affected upper limb synchronized with focal-side scalp acupuncture treatment; Control Group: rehabilitation training of the affected upper limb only. The intervention will last for 4 weeks, 5 times a week. Both acupuncture treatments will be performed according to the Revised Standards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA). The primary outcome indicators for this trial are Fugl-Meyer Assessment-Upper Extremity and Somatosensory Evoked Potential. Secondary outcome indicators include Wolf Motor Function Test, Upper Extremity Function Test, revised Nottingham Sensory Assessment Scale, Diffusion Tensor Imaging, and Modified Barthel Index. The incidence of adverse events will be used as the indicator of safety.

Discussion

The study will provide high-quality clinical evidence on whether ASRT improves upper limb motor and sensory function and activities of daily living (ADL) in stroke patients, and determine whether scalp acupuncture and intradermal acupuncture have synergistic effects.

Clinical trial registration

https://www.chictr.org.cn/, Chinese Clinical Trial Registry [ChiCTR2200066646].

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model's predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.

Motivation as a Measurable Outcome in Stroke Rehabilitation: A Systematic Review of the Literature

Motivated behaviours are thought to lead to enhanced performances. In the neurorehabilitation field, motivation has been demonstrated to be a link between cognition and motor performance, therefore playing an important role upon rehabilitation outcome determining factors. While motivation-enhancing interventions have been frequently investigated, a common and reliable motivation assessment strategy has not been established yet. This review aims to systematically explore and provide a comparison among the existing motivation assessment tools concerning stroke rehabilitation. For this purpose, a literature search (PubMed and Google Scholar) was performed, using the following Medical Subject Headings terms: "assessment" OR "scale" AND "motivation" AND "stroke" AND "rehabilitation". In all, 31 randomized clinical trials and 15 clinical trials were examined. The existing assessment tools can be grouped into two categories: the first mirroring the trade-off between patients and rehabilitation, the latter reflecting the link between patients and interventions. Furthermore, we presented assessment tools which reflect participation level or apathy, as an indirect index of motivation. In conclusion, we are left to put forth a possible common motivation assessment strategy, which might provide valuable incentive to investigate in future research.

Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review

Background

Brain–computer interfaces (BCI), initially designed to bypass the peripheral motor system to externally control movement using brain signals, are additionally being utilized for motor rehabilitation in stroke and other neurological disorders. Also called neurofeedback training, multiple approaches have been developed to link motor-related cortical signals to assistive robotic or electrical stimulation devices during active motor training with variable, but mostly positive, functional outcomes reported. Our specific research question for this scoping review was: for persons with non-progressive neurological injuries who have the potential to improve voluntary motor control, which mobile BCI-based neurofeedback methods demonstrate or are associated with improved motor outcomes for Neurorehabilitation applications?

Methods

We searched PubMed, Web of Science, and Scopus databases with all steps from study selection to data extraction performed independently by at least 2 individuals. Search terms included: brain machine or computer interfaces, neurofeedback and motor; however, only studies requiring a motor attempt, versus motor imagery, were retained. Data extraction included participant characteristics, study design details and motor outcomes.

Results

From 5109 papers, 139 full texts were reviewed with 23 unique studies identified. All utilized EEG and, except for one, were on the stroke population. The most commonly reported functional outcomes were the Fugl-Meyer Assessment (FMA; n = 13) and the Action Research Arm Test (ARAT; n = 6) which were then utilized to assess effectiveness, evaluate design features, and correlate with training doses. Statistically and functionally significant pre-to post training changes were seen in FMA, but not ARAT. Results did not differ between robotic and electrical stimulation feedback paradigms. Notably, FMA outcomes were positively correlated with training dose.

Conclusion

This review on BCI-based neurofeedback training confirms previous findings of effectiveness in improving motor outcomes with some evidence of enhanced neuroplasticity in adults with stroke. Associative learning paradigms have emerged more recently which may be particularly feasible and effective methods for Neurorehabilitation. More clinical trials in pediatric and adult neurorehabilitation to refine methods and doses and to compare to other evidence-based training strategies are warranted.

Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives

Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.

Recognizing the individualized sensorimotor loop of stroke patients during BMI-supported rehabilitation training based on brain functional connectivity analysis

BACKGROUND

Electroencephalogram (EEG) based brain-machine interaction training can facilitate rehabilitation by closing the sensorimotor loop. However, it remains unclear how to evaluate whether the loop is closed, especially for stroke patients whose brain regions of motor control and sensorimotor feedback could be altered. Our hypothesis is that motor recovery depends on whether sensorimotor loop is established poststroke. This study aims to explore how to evaluate the establishment of sensorimotor loop based on the evolving neural reorganization patterns after stroke.

NEW METHOD

14 stroke patients participated in the experiment and EEG were recorded during three specific tasks: Movement Imagery (MI), Passive Movement (PM) and Movement Execution (ME). Activated brain regions correlated with movement intention expression and sensorimotor feedback were detected respectively during MI and PM. In ME, local-averaged Phase Lag Index (PLI) was analyzed to represent the functional connectivity between activated brain regions of MI and PM.

RESULTS

Individualized cortical activation was found both in MI and PM. The overlapping brain activation during PM and MI did not correlate with patient's Fugl-Meyer Upper Extremity Motor Score (FMU) (p=0.26). However, we found that FMU of the group with higher local-averaged PLI was statistically higher than FMU of the group with lower local-averaged PLI compared with global-averaged PLI (p<0.05).

CONCLUSIONS

The findings demonstrate functional connectivity between activated brain regions of motor control and sensorimotor feedback may imply if the individualized sensorimotor loop is established poststroke. The successful formation of the closed loop can indicate stroke patients' motor recovery.

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.

Continuous Hybrid BCI Control for Robotic Arm Using Noninvasive Electroencephalogram, Computer Vision, and Eye Tracking

The controlling of robotic arms based on brain–computer interface (BCI) can revolutionize the quality of life and living conditions for individuals with physical disabilities. Invasive electroencephalography (EEG)-based BCI has been able to control multiple degrees of freedom (DOFs) robotic arms in three dimensions. However, it is still hard to control a multi-DOF robotic arm to reach and grasp the desired target accurately in complex three-dimensional (3D) space by a noninvasive system mainly due to the limitation of EEG decoding performance. In this study, we propose a noninvasive EEG-based BCI for a robotic arm control system that enables users to complete multitarget reach and grasp tasks and avoid obstacles by hybrid control. The results obtained from seven subjects demonstrated that motor imagery (MI) training could modulate brain rhythms, and six of them completed the online tasks using the hybrid-control-based robotic arm system. The proposed system shows effective performance due to the combination of MI-based EEG, computer vision, gaze detection, and partially autonomous guidance, which drastically improve the accuracy of online tasks and reduce the brain burden caused by long-term mental activities.

EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification

OBJECTIVE

Supernumerary Robotic Limbs (SRL) are body augmentation robotic devices by adding extra limbs or fingers to the human body different from the traditional wearable robotic devices such as prosthesis and exoskeleton. We proposed a novel MI (Motor imagery)-based BCI paradigm based on the sixth-finger which imagines controlling the extra finger movements. The goal of this work is to investigate the EEG characteristics and the application potential of MI-based BCI systems based on the new imagination paradigm (the sixth finger MI).

APPROACH

14 subjects participated in the experiment involving the sixth finger MI tasks and rest state. Event-related spectral perturbation (ERSP) was adopted to analyse EEG spatial features and key-channel time-frequency features. Common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM). A genetic algorithm (GA) was used to select combinations of EEG channels that maximized classification accuracy and verified EEG patterns based on the sixth finger MI. And we conducted a longitudinal 4-week EEG control experiment based on the new paradigm.

MAIN RESULTS

ERD (event-related desynchronization) was found in the supplementary motor area (SMA) and primary motor area (M1) with a faint contralateral dominance. Unlike traditional MI based on the human hand, ERD was also found in frontal lobe. GA results showed that the distribution of the optimal 8-channel is similar to EEG topographical distributions, nearing parietal and frontal lobe. And the classification accuracy based on the optimal 8-channel (the highest accuracy of 80% and mean accuracy of 70%) was significantly better than that based on the random 8-channel (p<0.01).

SIGNIFICANCE

This work provided a new paradigm for MI-based MI system and verified its feasibility, widened the control bandwidth of the BCI system.

Efficacy of Brain-Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials

Background. A number of recent randomized controlled trials reported the efficacy of brain-computer interface (BCI) for upper-limb stroke rehabilitation compared with other therapies. Despite the encouraging results reported, there is a significant variance in the reported outcomes. This paper aims to investigate the effectiveness of different BCI designs on poststroke upper-limb rehabilitation. Methods. The effect sizes of pooled and individual studies were assessed by computing Hedge's g values with a 95% confidence interval. Subgroup analyses were also performed to examine the impact of different BCI designs on the treatment effect. Results. The study included 12 clinical trials involving 298 patients. The analysis showed that the BCI yielded significant superior short-term and long-term efficacy in improving the upper-limb motor function compared to the control therapies (Hedge's g = 0.73 and 0.33, respectively). Based on our subgroup analyses, the BCI studies that used the intention of movement had a higher effect size compared to those used motor imagery (Hedge's g = 1.21 and 0.55, respectively). The BCI studies using band power features had a significantly higher effect size than those using filter bank common spatial patterns features (Hedge's g = 1.25 and - 0.23, respectively). Finally, the studies that used functional electrical stimulation as the BCI feedback had the highest effect size compared to other devices (Hedge's g = 1.2). Conclusion. This meta-analysis confirmed the effectiveness of BCI for upper-limb rehabilitation. Our findings support the use of band power features, the intention of movement, and the functional electrical stimulation in future BCI designs for poststroke upper-limb rehabilitation.

Rehabilitation of motor function in children with cerebral palsy based on motor imagery

To promote the rehabilitation of motor function in children with cerebral palsy (CP), we developed motor imagery (MI) based training system to assist their motor rehabilitation. Eighteen CP children, ten in short- and eight in long-term rehabilitation, participated in our study. In short-term rehabilitation, every 2 days, the MI datasets were collected; whereas the duration of two adjacency MI experiments was ten days in the long-term protocol. Meanwhile, within two adjacency experiments, CP children were requested to daily rehabilitate the motor function based on our system for 30 min. In both strategies, the promoted motor information processing was observed. In terms of the relative signal power spectra, a main effect of time was revealed, as the promoted power spectra were found for the last time of MI recording, compared to that of the first one, which first validated the effectiveness of our intervention. Moreover, as for network efficiency related to the motor information processing, compared to the first MI, the increased network properties were found for the last MI, especially in long-term rehabilitation in which CP children experienced a more obvious efficiency promotion. These findings did validate that our MI-based rehabilitation system has the potential for CP children to assist their motor rehabilitation.

A BCI-Based Vibrotactile Neurofeedback Training Improves Motor Cortical Excitability During Motor Imagery

In this study, we address the issue of whether vibrotactile feedback can enhance the motor cortex excitability translated into the plastic changes in local cortical areas during motor imagery (MI) BCI-based training. For this purpose, we focused on two of the most notable neurophysiological effects of MI - the event-related desynchronization (ERD) level and the increase in cortical excitability assessed with navigated transcranial magnetic stimulation (nTMS). For TMS navigation, we used individual high-resolution 3D brain MRIs. Ten BCI-naive and healthy adults participated in this study. The MI (rest or left/right hand imagery using Graz-BCI paradigm) tasks were performed separately in the presence and absence of feedback. To investigate how much the presence/absence of vibrotactile feedback in MI BCI-based training could contribute to the sensorimotor cortical activations, we compared the MEPs amplitude during MI after training with and without feedback. In addition, the ERD levels during MI BCI-based training were investigated. Our findings provide evidence that applying vibrotactile feedback during MI training leads to (i) an enhancement of the desynchronization level of mu-rhythm EEG patterns over the contralateral motor cortex area corresponding to the MI of the non-dominant hand; (ii) an increase in motor cortical excitability in hand muscle representation corresponding to a muscle engaged by the MI.

Design of a variable stiffness index finger exoskeleton

This paper presents the design and experimentation of a variable stiffness index finger exoskeleton consisting of four-bar mechanisms actuated by a linear actuator. The lengths of the four-bar mechanism were optimized so that it can follow a recorded index fingertip trajectory. The mechanism has a fixed compliance at the coupler of the four-bar link and a variable compliance at the linear actuator that moves the four-bar. The skeletal shape of the coupler of the finger link has been optimized using FEM. The exoskeleton can apply a constant fingertip force irrespective of the position of the fingers.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

Analysis of Prognostic Risk Factors Determining Poor Functional Recovery After Comprehensive Rehabilitation Including Motor-Imagery Brain-Computer Interface Training in Stroke Patients: A Prospective Study

Objective: Upper limb (UL) motor function recovery, especially distal function, is one of the main goals of stroke rehabilitation as this function is important to perform activities of daily living (ADL). The efficacy of the motor-imagery brain-computer interface (MI-BCI) has been demonstrated in patients with stroke. Most patients with stroke receive comprehensive rehabilitation, including MI-BCI and routine training. However, most aspects of MI-BCI training for patients with subacute stroke are based on routine training. Risk factors for inadequate distal UL functional recovery in these patients remain unclear; therefore, it is more realistic to explore the prognostic factors of this comprehensive treatment based on clinical practice. The present study aims to investigate the independent risk factors that might lead to inadequate distal UL functional recovery in patients with stroke after comprehensive rehabilitation including MI-BCI (CRIMI-BCI). Methods: This prospective study recruited 82 patients with stroke who underwent CRIMI-BCI. Motor-imagery brain-computer interface training was performed for 60 min per day, 5 days per week for 4 weeks. The primary outcome was improvement of the wrist and hand dimensionality of Fugl-Meyer Assessment (δFMA-WH). According to the improvement score, the patients were classified into the efficient group (EG, δFMA-WH > 2) and the inefficient group (IG, δFMA-WH ≤ 2). Binary logistic regression was used to analyze clinical and demographic data, including aphasia, spasticity of the affected hand [assessed by Modified Ashworth Scale (MAS-H)], initial UL function, age, gender, time since stroke (TSS), lesion hemisphere, and lesion location. Results: Seventy-three patients completed the study. After training, all patients showed significant improvement in FMA-UL (Z = 7.381, p = 0.000^**), FMA-SE (Z = 7.336, p = 0.000^**), and FMA-WH (Z = 6.568, p = 0.000^**). There were 35 patients (47.9%) in the IG group and 38 patients (52.1%) in the EG group. Multivariate analysis revealed that presence of aphasia [odds ratio (OR) 4.617, 95% confidence interval (CI) 1.435-14.860; p < 0.05], initial FMA-UL score ≤ 30 (OR 5.158, 95% CI 1.150-23.132; p < 0.05), and MAS-H ≥ level I+ (OR 3.810, 95% CI 1.231-11.790; p < 0.05) were the risk factors for inadequate distal UL functional recovery in patients with stroke after CRIMI-BCI. Conclusion: We concluded that CRIMI-BCI improved UL function in stroke patients with varying effectiveness. Inferior initial UL function, significant hand spasticity, and presence of aphasia were identified as independent risk factors for inadequate distal UL functional recovery in stroke patients after CRIMI-BCI.

Decoding Finger Tapping With the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution

In stroke rehabilitation, motor imagery based on a brain-computer interface is an extremely useful method to control an external device and utilize neurofeedback. Many studies have reported on the classification performance of motor imagery to decode individual fingers in stroke patients compared with healthy controls. However, classification performance for a given limb is still low because the differences between patients owing to brain reorganization after stroke are not considered. We used electroencephalography signals from eleven healthy controls and eleven stroke patients in this study. The subjects performed a finger tapping task during motor execution, and motor imagery was performed with the dominant and affected hands in the healthy controls and stroke patients, respectively. All fingers except for the thumb were classified using the proposed framework based on a voting module. The averaged four-class accuracies during motor execution and motor imagery were 53.16 ± 8.42% and 46.94 ± 5.99% for the healthy controls and 53.17 ± 14.09% and 66.00 ± 14.96% for the stroke patients, respectively. Importantly, the classification accuracies in the stroke patients were statistically higher than those in healthy controls during motor imagery. However, there was no significant difference between the accuracies of motor execution and motor imagery. These findings show the potential for high classification performance for a given limb during motor imagery in stroke patients. These results can also provide insights into controlling an external device on the basis of a brain-computer interface.

A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface

Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.

An Intelligent Human-Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves

The interactions between humans and unmanned aerial vehicles (UAVs), whose applications are increasing in the civilian field rather than for military purposes, are a popular future research area. Human–UAV interactions are a challenging problem because UAVs move in a three-dimensional space. In this paper, we present an intelligent human–UAV interaction approach in real time based on machine learning using wearable gloves. The proposed approach offers scientific contributions such as a multi-mode command structure, machine-learning-based recognition, task scheduling algorithms, real-time usage, robust and effective use, and high accuracy rates. For this purpose, two wearable smart gloves working in real time were designed. The signal data obtained from the gloves were processed with machine-learning-based methods and classified multi-mode commands were included in the human–UAV interaction process via the interface according to the task scheduling algorithm to facilitate sequential and fast operation. The performance of the proposed approach was verified on a data set created using 25 different hand gestures from 20 different people. In a test using the proposed approach on 49,000 datapoints, process time performance of a few milliseconds was achieved with approximately 98 percent accuracy.

Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review

BACKGROUND

Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we report the first systematic examination of the literature on the use of BCI-robot systems for the rehabilitation of fine motor skills associated with hand movement and profile these systems from a technical and clinical perspective.

METHODS

A search for January 2010-October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation at different stages of development involving tests on healthy participants or people who have had a stroke. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures.

RESULTS

30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for three BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery.

CONCLUSION

19 out of 30 studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. We identified large heterogeneity in reporting and emphasise the need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.

Motor Imagery Hand Movement Direction Decoding Using Brain Computer Interface to Aid Stroke Recovery and Rehabilitation

Motor Imagery (MI)-based Brain Computer Interface (BCI) system is a potential technology for active neurorehabilitation of stroke patients by complementing the conventional passive rehabilitation methods. Research to date mainly focused on classifying left vs. right hand/foot MI of stroke patients. Though a very few studies have reported decoding imagined hand movement directions using electroencephalogram (EEG)-based BCI, the experiments were conducted on healthy subjects. Our work analyzes MI-based brain cortical activity from EEG signals and decodes the imagined hand movement directions in stroke patients. The decoded direction (left vs. right) of hand movement imagination is used to provide control commands to a motorized arm support on which patient's affected (paralyzed) arm is placed. This enables the patient to move his/her stroke-affected hand towards the intended (imagined) direction that aids neuroplasticity in the brain. The synchronization measure called Phase Locking Value (PLV), extracted from EEG, is the neuronal signature used to decode the directional movement of the MI task. Event-related desynchronization/synchronization (ERD/ERS) analysis on Mu and Beta frequency bands of EEG is done to select the time bin corresponding to the MI task. The dissimilarities between the two directions of MI tasks are identified by selecting the most significant channel pairs that provided maximum difference in PLV features. The training protocol has an initial calibration session followed by a feedback session with 50 trials of MI task in each session. The feedback session extracts PLV features corresponding to most significant channel pairs which are identified in the calibration session and is used to predict the direction of MI task in left/right direction. An average MI direction classification accuracy of 74.44% is obtained in performing the training protocol and 68.63% from the prediction protocol during feedback session on 16 stroke patients.

Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data

OBJECTIVE

Magnetoencephalography (MEG) based brain-computer interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels (NoC) means an increase in computational complexity. However, not all sensors necessarily contribute significantly to an increase in classification accuracy (CA) and specifically in the case of MEG-based BCI no channel selection methodology has been performed. Therefore, this study investigates the effect of channel selection on the performance of MEG-based BCI.

APPROACH

MEG data were recorded for two sessions from 15 healthy participants performing motor imagery, cognitive imagery and a mixed imagery task pair using a unique paradigm. Performance of four state-of-the-art channel selection methods (i.e. Class-Correlation, ReliefF, Random Forest, and Infinite Latent Feature Selection were applied across six binary tasks in three different frequency bands) were evaluated in this study on two state-of-the-art features, i.e. bandpower and common spatial pattern (CSP).

MAIN RESULTS

All four methods provided a statistically significant increase in CA compared to a baseline method using all gradiometer sensors, i.e. 204 channels with band-power features from alpha (8-12 Hz), beta (13-30 Hz), or broadband (α + β) (8-30 Hz). It is also observed that the alpha frequency band performed better than the beta and broadband frequency bands. The performance of the beta band gave the lowest CA compared with the other two bands. Channel selection improved accuracy irrespective of feature types. Moreover, all the methods reduced the NoC significantly, from 204 to a range of 1-25, using bandpower as a feature and from 15 to 105 for CSP. The optimal channel number also varied not only in each session but also for each participant. Reducing the NoC will help to decrease the computational cost and maintain numerical stability in cases of low trial numbers.

SIGNIFICANCE

The study showed significant improvement in performance of MEG-BCI with channel selection irrespective of feature type and hence can be successfully applied for BCI applications.

Action observation training and brain-computer interface controlled functional electrical stimulation enhance upper extremity performance and cortical activation in patients with stroke: a randomized controlled trial

PURPOSE

Brain-computer interface (BCI)-functional electronic stimulation (FES) systems are increasingly being explored as potential neuro-rehabilitation tools. Here, we investigate the effect of action observation training (AOT) plus electroencephalogram (EEG)-based BCI-controlled FES system on motor recovery of upper extremity and cortical activation in patients with stroke.

METHOD

There were a total of 26 patients: an AOT plus BCI-FES group (n = 13) and a control group (n = 13). The control group performed FES treatment and the conventional physical therapy, while the AOT plus BCI-FES group performed AOT plus BCI-FES and the conventional physical therapy. Upper extremity performance was measured using the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Wolf Motor Function Test (WMFT), Motor Activity Log (MAL) and Modified Barthel Index (MBI). Cortical activation was measured using electro-encephalographic recordings from alpha and beta power, concentration, and activation.

RESULTS

After intervention, there were significant differences between two groups in FMA-UE, WMFT, MAL and MBI and the results of EEG including alpha power, beta power, concentration and activation.

CONCLUSIONS

This study demonstrated that AOT plus BCI-FES can enhance motor function of upper extremity and cortical activation in patients with stroke. This training method may be feasible and suitable for individuals with stroke.

Deep Learning Based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces

Inter-subject transfer learning is a long-standing problem in brain-computer interfaces (BCIs) and has not yet been fully realized due to high inter-subject variability in the brain signals related to motor imagery (MI). The recent success of deep learning-based algorithms in classifying different brain signals warrants further exploration to determine whether it is feasible for the inter-subject continuous decoding of MI signals to provide contingent neurofeedback which is important for neurorehabilitative BCI designs. In this paper, we have shown how a convolutional neural network (CNN) based deep learning framework can be used for inter-subject continuous decoding of MI related electroencephalographic (EEG) signals using the novel concept of Mega Blocks for adapting the network against inter-subject variabilities. These Mega Blocks have the capacity to repeat a specific architectural block several times such as one or more convolutional layers in a single Mega Block. The parameters of such Mega Blocks can be optimized using Bayesian hyperparameter optimization. The results, obtained on the publicly available BCI competition IV-2b dataset, yields an average inter-subject continuous decoding accuracy of 71.49% (κ = 0.42) and 70.84% (κ = 0.42) for two different training methods such as adaptive moment estimation (Adam) and stochastic gradient descent (SGDM), respectively, in 7 out of 9 subjects. Our results show for the first time that it is feasible to use CNN based architectures for inter-subject continuous decoding with a sufficient level of accuracy for developing calibration-free MI-BCIs for practical purposes.

A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback

Neurofeedback (NF) and brain-computer interface (BCI) applications rely on the registration and real-time feedback of individual patterns of brain activity with the aim of achieving self-regulation of specific neural substrates or control of external devices. These approaches have historically employed visual stimuli. However, in some cases vision is unsuitable or inadequately engaging. Other sensory modalities, such as auditory or haptic feedback have been explored, and multisensory stimulation is expected to improve the quality of the interaction loop. Moreover, for motor imagery tasks, closing the sensorimotor loop through haptic feedback may be relevant for motor rehabilitation applications, as it can promote plasticity mechanisms. This survey reviews the various haptic technologies and describes their application to BCIs and NF. We identify major trends in the use of haptic interfaces for BCI and NF systems and discuss crucial aspects that could motivate further studies.

Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis

BACKGROUND

A substantial number of clinical studies have demonstrated the functional recovery induced by the use of brain-computer interface (BCI) technology in patients after stroke. The objective of this review is to evaluate the effect sizes of clinical studies investigating the use of BCIs in restoring upper extremity function after stroke and the potentiating effect of transcranial direct current stimulation (tDCS) on BCI training for motor recovery.

METHODS

The databases (PubMed, Medline, EMBASE, CINAHL, CENTRAL, PsycINFO, and PEDro) were systematically searched for eligible single-group or clinical controlled studies regarding the effects of BCIs in hemiparetic upper extremity recovery after stroke. Single-group studies were qualitatively described, but only controlled-trial studies were included in the meta-analysis. The PEDro scale was used to assess the methodological quality of the controlled studies. A meta-analysis of upper extremity function was performed by pooling the standardized mean difference (SMD). Subgroup meta-analyses regarding the use of external devices in combination with the application of BCIs were also carried out. We summarized the neural mechanism of the use of BCIs on stroke.

RESULTS

A total of 1015 records were screened. Eighteen single-group studies and 15 controlled studies were included. The studies showed that BCIs seem to be safe for patients with stroke. The single-group studies consistently showed a trend that suggested BCIs were effective in improving upper extremity function. The meta-analysis (of 12 studies) showed a medium effect size favoring BCIs for improving upper extremity function after intervention (SMD = 0.42; 95% CI = 0.18-0.66; I² = 48%; P < 0.001; fixed-effects model), while the long-term effect (five studies) was not significant (SMD = 0.12; 95% CI = - 0.28 - 0.52; I² = 0%; P = 0.540; fixed-effects model). A subgroup meta-analysis indicated that using functional electrical stimulation as the external device in BCI training was more effective than using other devices (P = 0.010). Using movement attempts as the trigger task in BCI training appears to be more effective than using motor imagery (P = 0.070). The use of tDCS (two studies) could not further facilitate the effects of BCI training to restore upper extremity motor function (SMD = - 0.30; 95% CI = - 0.96 - 0.36; I² = 0%; P = 0.370; fixed-effects model).

CONCLUSION

The use of BCIs has significant immediate effects on the improvement of hemiparetic upper extremity function in patients after stroke, but the limited number of studies does not support its long-term effects. BCIs combined with functional electrical stimulation may be a better combination for functional recovery than other kinds of neural feedback. The mechanism for functional recovery may be attributed to the activation of the ipsilesional premotor and sensorimotor cortical network.

Home used, patient self-managed, brain-computer interface for the management of central neuropathic pain post spinal cord injury: usability study

BACKGROUND

Central Neuropathic Pain (CNP) is a frequent chronic condition in people with spinal cord injury (SCI). Previously, we showed that using laboratory brain-computer interface (BCI) technology for neurofeedback (NFB) training, it was possible to reduce CNP in people with SCI. In this study, we show results of patient self-managed treatment in their homes with a BCI-NFB using a consumer EEG device.

METHODS

Users: People with chronic SCI (17 M, 3 F, 50.6 ± 14.1 years old), and CNP ≥4 on a Visual Numerical Scale.

LOCATION

Laboratory training (up to 4 sessions) followed by home self-managed NFB. User Activity: Upregulating the EEG alpha band power by 10% above a threshold and at the same time downregulating the theta and upper beta (20-30 Hz) band power by 10% at electrode location C4. Technology: A consumer grade multichannel EEG headset (Epoch, Emotiv, USA), a tablet computer and custom made NFB software.

EVALUATION

EEG analysis, before and after NFB assessment, interviews and questionnaires.

RESULTS

Effectiveness: Out of 20 initially assessed participants, 15 took part in the study. Participants used the system for 6.9 ± 5.5 (median 4) weeks. Twelve participants regulated their brainwaves in a frequency specific manner and were most successful upregulating the alpha band power. However they typically upregulated power around their individual alpha peak (7.6 ± 0.8 Hz) that was lower than in people without CNP. The reduction in pain experienced was statistically significant in 12 and clinically significant (greater than 30%) in 8 participants. Efficiency: The donning was between 5 and 15 min, and approximately 10-20% of EEG data recorded in the home environment was noise. Participants were mildly stressed when self-administering NFB at home (2.4 on a scale 1-10). User satisfaction: Nine participants who completed the final assessment reported a high level of satisfaction (QUESQ, 4.5 ± 0.8), naming effectiveness, ease of use and comfort as main priorities. The main factors influencing frequency of NFB training were: health related issues, free time and pain intensity.

CONCLUSION

Portable NFB is a feasible solution for home-based self-managed treatment of CNP. Compared to pharmacological treatments, NFB has less side effects and provides users with active control over pain.

TRIAL REGISTRATION

GN15NE124 , Registered 9th June 2016.