Brain-Computer Interface-Controlled Exoskeletons in Clinical Neurorehabilitation: Ready or Not?

Master classes of the tenth international brain-computer interface meeting: showcasing the research of BCI trainees

The Tenth International brain-computer interface (BCI) meeting was held June 6-9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc & Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit. The topics of the master classes span the whole gamut of BCI research and techniques. These include data acquisition, neural decoding and analysis, invasive and noninvasive stimulation, and ethical and transitional considerations. Additionally, master classes spotlight innovations in BCI research. Herein, we discuss what was presented within the master classes by highlighting each trainee and expert researcher, providing relevant background information and results from each presentation, and summarizing discussion and references for further study.

Feasibility of a Neurorehabilitation Pipeline and an Automated Algorithm to Select Appropriate Treatments for Upper Extremity Motor Paralysis in Individuals With Chronic Stroke

OBJECTIVE

This study aimed to investigate the feasibility of a neurorehabilitation pipeline and develop an algorithm to automatically select the appropriate treatment for individuals with upper extremity motor paralysis after stroke in the chronic phase.

DESIGN

In experiment 1, eight post-stroke participants in the chronic phase who underwent treatment sustaining two to three phases were assessed before and after treatment. In experiment 2, a decision tree analysis was performed in which the dependent variable was set as the treatment option determined by a board-certified physiatrist for 95 poststroke participants; the independent variables were only motor function scores or both motor function scores and electromyogram variables.

RESULTS

In experiment 1, the clinical assessment scores were improved significantly after treatment. Experiment 2 showed that the agreements of the model with only motor function scores as the independent variable and with motor function scores and electromyogram variables as the independent variables were 75.8% and 82.1%, respectively.

CONCLUSIONS

This novel treatment package is feasible for improvement of motor function in poststroke individuals with severe motor paralysis. The study also established an automated algorithm for selecting appropriate treatments for upper extremity motor paralysis after stroke, identifying standard values of key variables, including electromyography variables.

The potential of lower limb exoskeletons to enhance life-space mobility and to leverage green exercise in the rehabilitation of older adults: an expert perspective

PURPOSE

As the global population aged 60+ grows, ensuring mobility and independence for older adults is a critical public health goal. This paper examines barriers to life-space mobility in older adults and explores wearable lower limb exoskeletons (LLEs) and green exercise as innovative solutions.

METHODS

Literature search and interdisciplinary expert input were utilized.

RESULTS

Life-space mobility, the physical space individuals move through daily, is often restricted by physical and environmental barriers, leading to increased dependency, depression, and reduced quality of life. LLEs offer promising support by compensating for decreased intrinsic capacities, thus facilitating outdoor mobility. Green exercise (i.e., physical activity in nature) provides mental and physical health benefits, further promoting mobility and well-being.

CONCLUSION

Combining LLEs with green exercise may create powerful rehabilitation modalities, addressing both physical and mental aspects of aging. Integrating life-space mobility research into exoskeleton development and rehabilitation programs ensures these solutions meet real-world needs, supporting Healthy Aging by maintaining functional ability. Future research should focus on user-centered development of optimized exoskeleton designs for outdoor use, developing tailored rehabilitation programs, and educating healthcare professionals and caregivers to maximize benefits. This integrated approach holds potential to significantly improve life-space mobility and quality of life for the aging population.

Exploring the Impact of Brain-Computer Interfaces on Health Care: Innovations, Challenges, and Future Prospects: A Review Article

Brain-Computer Interfaces (BCIs) are an innovative technology that methods with a great possibility to revolutionize the sphere of medicine with the help of integration of human brain and external devices. In this article, we discuss how BCIs can be incorporated into hospitals and civil rehabilitation centers, possibly for rehabilitation, communication, and cognitive treatments. This review aims to discuss the advancement, usefulness, difficulties, and potential in regards to the use of BCIs in healthcare. We describe trends in the development of BCIs from simple experimental paradigms to multimedia advanced devices and their usage in clinical practice: assistive technology in patients with motor disorders, neurorehabilitation of post-stroke patients, and cognitive prosthesis for humans with neurodegenerative diseases. The article also emphasizes on present-day issues including signal quality, comfort level of the users, and the ethical parameter of the technique along with the research going on and future work streams. Thus, by evaluating the modern developments in the field and highlighting the existing problems, this article will try to give a briefing on the current stage of application of BCIs in the sphere of healthcare.

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.

HYBRIDMINDS-summary and outlook of the 2023 international conference on the ethics and regulation of intelligent neuroprostheses

Neurotechnology and Artificial Intelligence (AI) have developed rapidly in recent years with an increasing number of applications and AI-enabled devices that are about to enter the market. While promising to substantially improve quality of life across various severe medical conditions, there are also concerns that the convergence of these technologies, e.g., in the form of intelligent neuroprostheses, may have undesirable consequences and compromise cognitive liberty, mental integrity, or mental privacy. Therefore, various international organizations, such as the Organization for Economic Cooperation and Development (OECD) or United Nations Educational, Scientific and Cultural Organization (UNESCO), have formed initiatives to tackle such questions and develop recommendations that mitigate risks while fostering innovation. In this context, a first international conference on the ethics and regulation of intelligent neuroprostheses was held in Berlin, Germany, in autumn 2023. The conference gathered leading experts in neuroscience, engineering, ethics, law, philosophy as well as representatives of industry, policy making and the media. Here, we summarize the highlights of the conference, underline the areas in which a broad consensus was found among participants, and provide an outlook on future challenges in development, deployment, and regulation of intelligent neuroprostheses.

Dystrophin 71 deficiency causes impaired aquaporin-4 polarization contributing to glymphatic dysfunction and brain edema in cerebral ischemia

OBJECTIVE

The glymphatic system serves as a perivascular pathway that aids in clearing liquid and solute waste from the brain, thereby enhancing neurological function. Disorders in glymphatic drainage contribute to the development of vasogenic edema following cerebral ischemia, although the molecular mechanisms involved remain poorly understood. This study aims to determine whether a deficiency in dystrophin 71 (DP71) leads to aquaporin-4 (AQP4) depolarization, contributing to glymphatic dysfunction in cerebral ischemia and resulting in brain edema.

METHODS

A mice model of middle cerebral artery occlusion and reperfusion was used. A fluorescence tracer was injected into the cortex and evaluated glymphatic clearance. To investigate the role of DP71 in maintaining AQP4 polarization, an adeno-associated virus with the astrocyte promoter was used to overexpress Dp71. The expression and distribution of DP71 and AQP4 were analyzed using immunoblotting, immunofluorescence, and co-immunoprecipitation techniques. The behavior ability of mice was evaluated by open field test. Open-access transcriptome sequencing data were used to analyze the functional changes of astrocytes after cerebral ischemia. MG132 was used to inhibit the ubiquitin-proteasome system. The ubiquitination of DP71 was detected by immunoblotting and co-immunoprecipitation.

RESULTS

During the vasogenic edema stage following cerebral ischemia, a decline in the efflux of interstitial fluid tracer was observed. DP71 and AQP4 were co-localized and interacted with each other in the perivascular astrocyte endfeet. After cerebral ischemia, there was a notable reduction in DP71 protein expression, accompanied by AQP4 depolarization and proliferation of reactive astrocytes. Increased DP71 expression restored glymphatic drainage and reduced brain edema. AQP4 depolarization, reactive astrocyte proliferation, and the behavior of mice were improved. After cerebral ischemia, DP71 was degraded by ubiquitination, and MG132 inhibited the decrease of DP71 protein level.

CONCLUSION

AQP4 depolarization after cerebral ischemia leads to glymphatic clearance disorder and aggravates cerebral edema. DP71 plays a pivotal role in regulating AQP4 polarization and consequently influences glymphatic function. Changes in DP71 expression are associated with the ubiquitin-proteasome system. This study offers a novel perspective on the pathogenesis of brain edema following cerebral ischemia.

Striking the Balance: Embracing Technology While Upholding Humanistic Principles in Neurorehabilitation

BACKGROUND

The rapid advancement of technology-focused strategies in neurorehabilitation has brought optimism to individuals with neurological disorders, caregivers, and physicians while reshaping medical practice and training.

OBJECTIVES

We critically examine the implications of technology in neurorehabilitation, drawing on discussions from the 2021 and 2024 World Congress for NeuroRehabilitation. While acknowledging the value of technology, it highlights inherent limitations and ethical concerns, particularly regarding the potential overshadowing of humanistic approaches. The integration of technologies such as robotics, artificial intelligence, neuromodulation, and brain-computer interfaces enriches neurorehabilitation by offering interdisciplinary solutions. However, ethical considerations arise regarding the balance between compensation for deficits, accessibility of technologies, and their alignment with fundamental principles of care. Additionally, the pitfalls of relying solely on neuroimaging data are discussed, stressing the necessity for a more comprehensive understanding of individual variability and clinical skills in rehabilitation.

RESULTS

From a clinical perspective, the article advocates for realistic solutions that prioritize individual needs, quality of life, and social inclusion over technological allure. It underscores the importance of modesty and honesty in responding to expectations while emphasizing the uniqueness of each individual's experience. Moreover, it argues for the preservation of human-centric approaches alongside technological advancements, recognizing the invaluable role of clinical observation and human interaction in rehabilitation.

CONCLUSION

Ultimately, the article calls for a balanced attitude that integrates both scientific and humanistic perspectives in neurorehabilitation. It highlights the symbiotic relationship between the sciences and humanities, advocating for philosophical questioning to guide the ethical implementation of new technologies and foster interdisciplinary dialogue.

[An emerging discipline: brain-computer interfaces medicine]

With the development of brain-computer interface (BCI) technology and its translational application in clinical medicine, BCI medicine has emerged, ushering in profound changes to the practice of medicine, while also bringing forth a series of ethical issues related to BCI medicine. BCI medicine is progressively emerging as a new disciplinary focus, yet to date, there has been limited literature discussing it. Therefore, this paper focuses on BCI medicine, firstly providing an overview of the main potential medical applications of BCI technology. It then defines the discipline, outlines its objectives, methodologies, potential efficacy, and associated translational medical research. Additionally, it discusses the ethics associated with BCI medicine, and introduces the standardized operational procedures for BCI medical applications and the methods for evaluating the efficacy of BCI medical applications. Finally, it anticipates the challenges and future directions of BCI medicine. In the future, BCI medicine may become a new academic discipline or major in higher education. In summary, this article is hoped to provide thoughts and references for the development of the discipline of BCI medicine.

Tracing the evolving dynamics and research hotspots of spinal cord injury and surgical decompression from 1975 to 2024: a bibliometric analysis

Background

Exploration of the benefits and timing of surgical decompression in spinal cord injury (SCI) has been a research hotspot. However, despite the higher volume and increasing emphasis on quality there remains no bibliometric view on SCI and surgical decompression. In this study, we aimed to perform bibliometric analysis to reveal the core countries, affiliations, journals, authors, and developmental trends in SCI and surgical decompression across the past 50 years.

Methods

Articles and reviews were retrieved from web of science core collection between 1975 and 2024. The bibliometrix package in R was used for data analysis and visualizing.

Results

A total of 8,688 documents were investigated, indicating an ascending trend in annual publications. The USA and China played as the leaders in scientific productivity. The University of Toronto led in institutional productions. Core authors, such as Michael G. Fehlings, showed high productivity, and occasional authors showed widespread interests. Core journals like Spine and Spinal Cord served as beacons in this field. The interaction of core authors and international collaboration accentuated the cross-disciplinary feature of the field. Prominent documents emphasized the clinical significance of early decompression in 24 h post SCI.

Conclusion

Based on comprehensive bibliometric analysis and literature review, we identified the hotspots and future directions of this field: (1) further investigation into the molecular and cellular mechanisms to provide pre-clinical evidence for biological effects of early surgical decompression in SCI animal models; (2) further evaluation and validation of the optimal time window of surgical decompression based on large cohort, considering the inherent heterogeneity of subpopulations in complicated immune responses post SCI; (3) further exploration on the benefits of early decompression on the neurological, functional, and clinical outcomes in acute SCI; (4) evaluation of the optimal surgical methods and related outcomes; (5) applications of artificial intelligence-based technologies in spinal surgical decompression.

Preparatory movement state enhances premovement EEG representations for brain-computer interfaces

Objective. Motor-related brain-computer interface (BCI) have a broad range of applications, with the detection of premovement intentions being a prominent use case. However, the electroencephalography (EEG) features during the premovement phase are not distinctly evident and are susceptible to attentional influences. These limitations impede the enhancement of performance in motor-based BCI. The objective of this study is to establish a premovement BCI encoding paradigm that integrates the preparatory movement state and validates its feasibility in improving the detection of movement intentions.Methods. Two button tasks were designed to induce subjects into a preparation state for two movement intentions (left and right) based on visual guidance, in contrast to spontaneous premovement. The low frequency movement-related cortical potentials (MRCPs) and high frequency event-related desynchronization (ERD) EEG data of 14 subjects were recorded. Extracted features were fused and classified using task related common spatial patterns (CSP) and CSP algorithms. Differences between prepared premovement and spontaneous premovement were compared in terms of time domain, frequency domain, and classification accuracy.Results. In the time domain, MRCPs features reveal that prepared premovement induce lower amplitude and earlier latency on both contralateral and ipsilateral motor cortex compared to spontaneous premovement, with susceptibility to the dominant hand's influence. Frequency domain ERD features indicate that prepared premovement induce lower ERD values bilaterally, and the ERD recovery speed after button press is the fastest. By using the fusion approach, the classification accuracy increased from 78.92% for spontaneous premovement to 83.59% for prepared premovement (p< 0.05). Along with the 4.67% improvement in classification accuracy, the standard deviation decreased by 0.95.Significance. The research findings confirm that incorporating a preparatory state into premovement enhances neural representations related to movement. This encoding enhancement paradigm effectively improves the performance of motor-based BCI. Additionally, this concept has the potential to broaden the range of decodable movement intentions and related information in motor-related BCI.

Peripheral nerve transfers for dysfunctions in central nervous system injuries: a systematic review

BACKGROUND

The review highlights recent advancements and innovative uses of nerve transfer surgery in treating dysfunctions caused by central nervous system (CNS) injuries, with a particular focus on spinal cord injury (SCI), stroke, traumatic brain injury, and cerebral palsy.

METHODS

A comprehensive literature search was conducted regarding nerve transfer for restoring sensorimotor functions and bladder control following injuries of spinal cord and brain, across PubMed and Web of Science from January 1920 to May 2023. Two independent reviewers undertook article selection, data extraction, and risk of bias assessment with several appraisal tools, including the Cochrane Risk of Bias Tool, the JBI Critical Appraisal Checklist, and SYRCLE's ROB tool. The study protocol has been registered and reported following PRISMA and AMSTAR guidelines.

RESULTS

Nine hundred six articles were retrieved, of which 35 studies were included (20 on SCI and 15 on brain injury), with 371 participants included in the surgery group and 192 in the control group. These articles were mostly low-risk, with methodological concerns in study types, highlighting the complexity and diversity. For SCI, the strength of target muscle increased by 3.13 of Medical Research Council grade, and the residual urine volume reduced by more than 100 ml in 15 of 20 patients. For unilateral brain injury, the Fugl-Myer motor assessment (FMA) improved 15.14-26 score in upper extremity compared to 2.35-26 in the control group. The overall reduction in Modified Ashworth score was 0.76-2 compared to 0-1 in the control group. Range of motion (ROM) increased 18.4-80° in elbow, 20.4-110° in wrist and 18.8-130° in forearm, while ROM changed -4.03°-20° in elbow, -2.08°-10° in wrist, -2.26°-20° in forearm in the control group. The improvement of FMA in lower extremity was 9 score compared to the presurgery.

CONCLUSION

Nerve transfer generally improves sensorimotor functions in paralyzed limbs and bladder control following CNS injury. The technique effectively creates a 'bypass' for signals and facilitates functional recovery by leveraging neural plasticity. It suggested a future of surgery, neurorehabilitation and robotic-assistants converge to improve outcomes for CNS.

Bionic Spider Web Flexible Strain Sensor Based on CF-L and Machine Learning

At present, the preparation of laser-induced graphene (LIG) has become an important technology in sensor manufacturing. In the conventional preparation process, the CO2 laser is widely used; however, its experimental period is long and its efficiency needs to be improved. We propose an innovative strategy to improve the experimental efficiency. We use the machine learning method to accurately predict the preparation parameters of LIG, so as to optimize the experimental process. Different structures can lead to different sensor performances. The structure constructed by the CO2 laser is rough and has a large size, which can affect the performance of the sensor. Therefore, we propose for the first time an innovative method for intramembrane structure construction that combines the advantages of the CO2 laser and fiber laser (CF-L). With this CF-L method, we have successfully prepared a biomimetic, flexible strain sensor. This sensor not only maintains a high degree of sensitivity, but also has a more refined and optimized structure. The manufacturing process of the whole sensor is simple, economical, and durable and can be prepared in large quantities and can be used to detect the extension and bending of human joints.

Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study

BACKGROUND

This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will.

METHODS

A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding.

RESULTS

The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance.

CONCLUSION

This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.

Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration-a bibliometrics and visualization analysis

Background

Artificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research's progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions.

Methods

We used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration.

Results

A total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF = 4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas.

Conclusion

We pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth.

Fabrication of Laser-Induced Graphene Based Flexible Sensors Using 355 nm Ultraviolet Laser and Their Application in Human-Computer Interaction System

In recent years, flexible sensors based on laser-induced graphene (LIG) have played an important role in areas such as smart healthcare, smart skin, and wearable devices. This paper presents the fabrication of flexible sensors based on LIG technology and their applications in human-computer interaction (HCI) systems. Firstly, LIG with a sheet resistance as low as 4.5 Ω per square was generated through direct laser interaction with commercial polyimide (PI) film. The flexible sensors were then fabricated through a one-step method using the as-prepared LIG. The applications of the flexible sensors were demonstrated by an HCI system, which was fabricated through the integration of the flexible sensors and a flexible glove. The as-prepared HCI system could detect the bending motions of different fingers and translate them into the movements of the mouse on the computer screen. At the end of the paper, a demonstration of the HCI system is presented in which words were typed on a computer screen through the bending motion of the fingers. The newly designed LIG-based flexible HCI system can be used by persons with limited mobility to control a virtual keyboard or mouse pointer, thus enhancing their accessibility and independence in the digital realm.

Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction

Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.

The neuromuscular control for lower limb exoskeleton- a 50-year perspective

Historically, impaired lower limb function has resulted in heavy health burden and large economic loss in society. Although experts from various fields have put large amounts of effort into overcoming this challenge, there is still not a single standard treatment that can completely restore the lost limb function. During the past half century, with the advancing understanding of human biomechanics and engineering technologies, exoskeletons have achieved certain degrees of success in assisting and rehabilitating patients with loss of limb function, and therefore has been spotlighted in both the medical and engineering fields. In this article, we review the development milestones of lower limb exoskeletons as well as the neuromuscular interactions between the device and wearer throughout the past 50 years. Fifty years ago, the lower-limb exoskeletons just started to be devised. We review several prototypes and present their designs in terms of structure, sensor and control systems. Subsequently, we introduce the development milestones of modern lower limb exoskeletons and discuss the pros and cons of these differentiated devices. In addition, we summarize current important neuromuscular control systems and sensors; and discuss current evidence demonstrating how the exoskeletons may affect neuromuscular control of wearers. In conclusion, based on our review, we point out the possible future direction of combining multiple current technologies to build lower limb exoskeletons that can serve multiple aims.

Global synergistic actions to improve brain health for human development

The global burden of neurological disorders is substantial and increasing, especially in low-resource settings. The current increased global interest in brain health and its impact on population wellbeing and economic growth, highlighted in the World Health Organization's new Intersectoral Global Action Plan on Epilepsy and other Neurological Disorders 2022-2031, presents an opportunity to rethink the delivery of neurological services. In this Perspective, we highlight the global burden of neurological disorders and propose pragmatic solutions to enhance neurological health, with an emphasis on building global synergies and fostering a 'neurological revolution' across four key pillars - surveillance, prevention, acute care and rehabilitation - termed the neurological quadrangle. Innovative strategies for achieving this transformation include the recognition and promotion of holistic, spiritual and planetary health. These strategies can be deployed through co-design and co-implementation to create equitable and inclusive access to services for the promotion, protection and recovery of neurological health in all human populations across the life course.

Synchronous motor imagery and visual feedback of finger movement elicit the moving rubber hand illusion, at least in illusion-susceptible individuals

Recent evidence suggests that imagined auditory and visual sensory stimuli can be integrated with real sensory information from a different sensory modality to change the perception of external events via cross-modal multisensory integration mechanisms. Here, we explored whether imagined voluntary movements can integrate visual and proprioceptive cues to change how we perceive our own limbs in space. Participants viewed a robotic hand wearing a glove repetitively moving its right index finger up and down at a frequency of 1 Hz, while they imagined executing the corresponding movements synchronously or asynchronously (kinesthetic-motor imagery); electromyography (EMG) from the participants' right index flexor muscle confirmed that the participants kept their hand relaxed while imagining the movements. The questionnaire results revealed that the synchronously imagined movements elicited illusory ownership and a sense of agency over the moving robotic hand-the moving rubber hand illusion-compared with asynchronously imagined movements; individuals who affirmed experiencing the illusion with real synchronous movement also did so with synchronous imagined movements. The results from a proprioceptive drift task further demonstrated a shift in the perceived location of the participants' real hand toward the robotic hand in the synchronous versus the asynchronous motor imagery condition. These results suggest that kinesthetic motor imagery can be used to replace veridical congruent somatosensory feedback from a moving finger in the moving rubber hand illusion to trigger illusory body ownership and agency, but only if the temporal congruence rule of the illusion is obeyed. This observation extends previous studies on the integration of mental imagery and sensory perception to the case of multisensory bodily awareness, which has potentially important implications for research into embodiment of brain-computer interface controlled robotic prostheses and computer-generated limbs in virtual reality.

The Berlin Bimanual Test for Tetraplegia (BeBiTT): development, psychometric properties, and sensitivity to change in assistive hand exoskeleton application

BACKGROUND

Assistive hand exoskeletons are promising tools to restore hand function after cervical spinal cord injury (SCI) but assessing their specific impact on bimanual hand and arm function is limited due to lack of reliable and valid clinical tests. Here, we introduce the Berlin Bimanual Test for Tetraplegia (BeBiTT) and demonstrate its psychometric properties and sensitivity to assistive hand exoskeleton-related improvements in bimanual task performance.

METHODS

Fourteen study participants with subacute cervical SCI performed the BeBiTT unassisted (baseline). Thereafter, participants repeated the BeBiTT while wearing a brain/neural hand exoskeleton (B/NHE) (intervention). Online control of the B/NHE was established via a hybrid sensorimotor rhythm-based brain-computer interface (BCI) translating electroencephalographic (EEG) and electrooculographic (EOG) signals into open/close commands. For reliability assessment, BeBiTT scores were obtained by four independent observers. Besides internal consistency analysis, construct validity was assessed by correlating baseline BeBiTT scores with the Spinal Cord Independence Measure III (SCIM III) and Quadriplegia Index of Function (QIF). Sensitivity to differences in bimanual task performance was assessed with a bootstrapped paired t-test.

RESULTS

The BeBiTT showed excellent interrater reliability (intraclass correlation coefficients > 0.9) and internal consistency (α = 0.91). Validity of the BeBiTT was evidenced by strong correlations between BeBiTT scores and SCIM III as well as QIF. Wearing a B/NHE (intervention) improved the BeBiTT score significantly (p < 0.05) with high effect size (d = 1.063), documenting high sensitivity to intervention-related differences in bimanual task performance.

CONCLUSION

The BeBiTT is a reliable and valid test for evaluating bimanual task performance in persons with tetraplegia, suitable to assess the impact of assistive hand exoskeletons on bimanual function.

Locked-in syndrome revisited

The locked-in syndrome (LiS) is characterized by quadriplegia with preserved vertical eye and eyelid movements and retained cognitive abilities. Subcategorization, aetiologies and the anatomical foundation of LiS are discussed. The damage of different structures in the pons, mesencephalon and thalamus are attributed to symptoms of classical, complete and incomplete LiS and the locked-in plus syndrome, which is characterized by additional impairments of consciousness, making the clinical distinction to other chronic disorders of consciousness at times difficult. Other differential diagnoses are cognitive motor dissociation (CMD) and akinetic mutism. Treatment options are reviewed and an early, interdisciplinary and aggressive approach, including the provision of psychological support and coping strategies is favoured. The establishment of communication is a main goal of rehabilitation. Finally, the quality of life of LiS patients and ethical implications are considered. While patients with LiS report a high quality of life and well-being, medical professionals and caregivers have largely pessimistic perceptions. The negative view on life with LiS must be overthought and the autonomy and dignity of LiS patients prioritized. Knowledge has to be disseminated, diagnostics accelerated and technical support system development promoted. More well-designed research but also more awareness of the needs of LiS patients and their perception as individual persons is needed to enable a life with LiS that is worth living.

Personalized Brain-Computer Interface and Its Applications

Brain-computer interfaces (BCIs) are a new technology that subverts traditional human-computer interaction, where the control signal source comes directly from the user's brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.