Research on two-class and four-class action recognition based on EEG signals

BMI has attracted widespread attention in the past decade, which has greatly improved the living conditions of patients with motor disorders. The application of EEG signals in lower limb rehabilitation robots and human exoskeleton has also been gradually applied by researchers. Therefore, the recognition of EEG signals is of great significance. In this paper, a CNN-LSTM neural network model is designed to study the two-class and four-class motion recognition of EEG signals. In this paper, a brain-computer interface experimental scheme is designed. Combining the characteristics of EEG signals, the time-frequency characteristics of EEG signals and event-related potential phenomena are analyzed, and the ERD/ERS characteristics are obtained. Pre-process EEG signals, and propose a CNN-LSTM neural network model to classify the collected binary and four-class EEG signals. The experimental results show that the CNN-LSTM neural network model has a good effect, and its average accuracy and kappa coefficient are higher than the other two classification algorithms, which also shows that the classification algorithm selected in this paper has a good classification effect.

A Gravity-Compensated Upper-Limb Exoskeleton for Functional Rehabilitation of the Shoulder Complex

In the last decade, several exoskeletons for shoulder rehabilitation have been presented in the literature. Most of these devices focus on the shoulder complex and limit the normal mobility of the rest of the body, forcing the patient into a fixed standing or sitting position. Nevertheless, this severely limits the range of activities that can potentially be simulated during the rehabilitation, preventing the execution of occupational therapy which involves the execution of tasks based on activities of daily living (ADLs). These tasks involve different muscular groups and whole-body movements, such as, e.g., picking up objects from the ground. To enable whole-body functional rehabilitation, the challenge is to shift the paradigm of robotic rehabilitation towards machines that can enable wide workspaces and high mobility. In this perspective, here we present Float: an upper-limb exoskeleton designed to promote and accelerate the motor and functional recovery of the shoulder joint complex following post-traumatic or post-surgical injuries. Indeed, Float allows the patient to move freely in a very large workspace. The key component that enables this is a passive polyarticulated arm which supports the total exoskeleton weight and allows the patient to move freely in space, empowering rehabilitation through a deeper interaction with the surrounding environment. A characterization of the reachable workspace of both the exoskeleton and the polyarticulated passive arm is presented. These results support the conclusion that a patient wearing Float can perform a wide variety of ADLs without bearing its weight.

Meeting Proceedings for SCI 2020: Launching a Decade of Disruption in Spinal Cord Injury Research

The spinal cord injury (SCI) research community has experienced great advances in discovery research, technology development, and promising clinical interventions in the past decade. To build upon these advances and maximize the benefit to persons with SCI, the National Institutes of Health (NIH) hosted a conference February 12-13, 2019 titled "SCI 2020: Launching a Decade of Disruption in Spinal Cord Injury Research." The purpose of the conference was to bring together a broad range of stakeholders, including researchers, clinicians and healthcare professionals, persons with SCI, industry partners, regulators, and funding agency representatives to break down existing communication silos. Invited speakers were asked to summarize the state of the science, assess areas of technological and community readiness, and build collaborations that could change the trajectory of research and clinical options for people with SCI. In this report, we summarize the state of the science in each of five key domains and identify the gaps in the scientific literature that need to be addressed to move the field forward.

A pilot randomized controlled trial of robotic exoskeleton-assisted exercise rehabilitation in multiple sclerosis

BACKGROUND

Co-occurring mobility and cognitive impairments are common, debilitating, and poorly-managed with pharmacological therapies in persons with multiple sclerosis (MS). Exercise rehabilitation (ER), particularly walking ER, has been suggested as one of the best approaches for managing these manifestations of MS. However, there is a focal lack of efficacy of ER on mobility and cognitive outcomes in persons with MS who present with substantial neurological disability. Such severe neurological disability oftentimes precludes the ability for participation in highly-intensive and repetitive ER that is necessary for eliciting adaptations in mobility and cognition. To address such a concern, robotic exoskeleton-assisted ER (REAER) might represent a promising intervention approach for managing co-occurring mobility and cognitive impairments in those with substantial MS disability who might not benefit from traditional ER.

METHODS

The current pilot single-blind, randomized controlled trial (RCT) compared the effects of 4-weeks of REAER with 4-weeks of conventional gait training (CGT) as a standard-of-care control condition on functional mobility (timed up-and-go; TUG), walking endurance (six-minute walk test; 6MWT), cognitive processing speed (CPS; Symbol Digit Modalities Test; SDMT), and brain connectivity (thalamocortical resting-state functional connectivity (RSFC) based on fMRI) outcomes in 10 persons with substantial MS-related neurological disability.

RESULTS

Overall, compared with CGT, 4-weeks of REAER was associated with large improvements in functional mobility (η_p²=.38), CPS (η_p²=.53), and RSFC between the thalamus and ventromedial prefrontal cortex (η_p²=.72), but not walking endurance (η_p²=.01). Further, changes in RSFC were moderately associated with changes in TUG, 6MWT, and SDMT performance, respectively, whereby increased thalamocortical RSFC was associated with improved functional mobility, walking endurance, and CPS (|ρ|>.36).

CONCLUSION

The current pilot RCT provides initial support for REAER as an approach for improving functional mobility and CPS, perhaps based on adaptive and integrative central nervous system plasticity, namely increases in RSFC between the thalamus and ventromedial prefrontal cortex, in a small sample of persons with substantial MS disability. Such a pilot trial provides proof-of-concept data for the design and implementation of an appropriately-powered RCT of REAER in a larger sample of persons with MS who present with co-occurring impairments in both mobility and cognitive functioning.

A Taxonomy of Ethical, Legal and Social Implications of Wearable Robots: An Expert Perspective

Wearable robots and exoskeletons are relatively new technologies designed for assisting and augmenting human motor functions. Due to their different possible design applications and their intimate connection to the human body, they come with specific ethical, legal, and social issues (ELS), which have not been much explored in the recent ELS literature. This paper draws on expert consultations and a literature review to provide a taxonomy of the most important ethical, legal, and social issues of wearable robots. These issues are categorized in (1) wearable robots and the self, (2) wearable robots and the other, and (3) wearable robots in society.

The Wearable Co-Design Domino: A User-Centered Methodology to Co-Design and Co-Evaluate Wearables

This paper presents a user-centered methodology to co-design and co-evaluate wearables that has been developed following a research-through design methodology. It has been based on the principles of human–computer interaction and on an empirical case entitled “Design and Development of a Low-Cost Wearable Glove to Track Forces Exerted by Workers in Car Assembly Lines” published in Sensors. Insights from both studies have been used to develop the wearable co-design domino presented in this study. The methodology consists of different design stages composed of an ideation stage, digital service development and test stages, hardware development and test stage, and a final test stage. The main conclusions state that it is necessary to maintain a close relationship between human factors and technical factors when designing wearable. Additionally, through the several studies, it has been concluded that there is need of different field experts that should co-design and co-evaluate wearable iteratively and involving users from the beginning of the process.

Wearable Design Requirements Identification and Evaluation

Wearable electronics make it possible to monitor human activity and behavior. Most of these devices have not taken into account human factors and they have instead focused on technological issues. This fact could not only affect human–computer interaction and user experience but also the devices’ use cycle. Firstly, this paper presents a classification of wearable design requirements that have been carried out by combining a quantitative and a qualitative methodology. Secondly, we present some evaluation procedures based on design methodologies and human–computer interaction measurement tools. Thus, this contribution aims to provide a roadmap for wearable designers and researchers in order to help them to find more efficient processes by providing a classification of the design requirements and evaluation tools. These resources represent time and resource-saving contributions. Therefore designers and researchers do not have to review the literature. It will no be necessary to carry out exploratory studies for the purposes of identifying requirements or evaluation tools either.

Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation

The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.

Academic Review and Perspectives on Robotic Exoskeletons

Since the first robotic exoskeleton was developed in 1960, this research field has attracted much interest from both the academic and industrial communities resulting in scientific publications, prototype developments and commercialized products. In this article, to document the progress in and current status of this field, we performed a bibliometric analysis. This analysis evaluated the publications in the field of robotic exoskeletons from 1990 to July 2019 that were retrieved from the Science Citation Index Expanded database. The bibliometric analyses were presented in terms of author keywords, year, country, institution, journal, author, and the citation. Results show that currently the United States has taken the leading position in this field and has built the largest collaborative network with other countries. The Massachusetts Institute of Technology (MIT) made the greatest contribution to the field of robotic exoskeleton investigations in terms of the number of publications, average citations per publication and the h-index. In addition, the Journal of NeuroEngineering and Rehabilitation ranks first among the top 20 academic journals in terms of the number of publications related to robotic exoskeletons during the period investigated. Author keyword analysis indicates that most research has focused on rehabilitation robotics. Biomedical engineering, rehabilitation and the neurosciences are the most common disciplines conducting research in this area according to the Web of Science (WoS). Our study comprehensively assesses the current research status and collaboration network of robotic exoskeletons, thus helping researchers steer their projects or locate potential collaborators.

Brain-machine interfaces for controlling lower-limb powered robotic systems

OBJECTIVE

Lower-limb, powered robotics systems such as exoskeletons and orthoses have emerged as novel robotic interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs). A number of preliminary studies have been published on this topic, but a systematic understanding of the experimental design, tasks, and performance of BMI-exoskeleton systems for restoration of gait is lacking.

APPROACH

To address this gap, we applied standard systematic review methodology for a literature search in PubMed and EMBASE databases and identified 11 studies involving BMI-robotics systems. The devices, user population, input and output of the BMIs and robot systems respectively, neural features, decoders, denoising techniques, and system performance were reviewed and compared.

MAIN RESULTS

Results showed BMIs classifying walk versus stand tasks are the most common. The results also indicate that electroencephalography (EEG) is the only recording method for humans. Performance was not clearly presented in most of the studies. Several challenges were summarized, including EEG denoising, safety, responsiveness and others.

SIGNIFICANCE

We conclude that lower-body powered exoskeletons with automated gait intention detection based on BMIs open new possibilities in the assistance and rehabilitation fields, although the current performance, clinical benefits and several key challenging issues indicate that additional research and development is required to deploy these systems in the clinic and at home. Moreover, rigorous EEG denoising techniques, suitable performance metrics, consistent trial reporting, and more clinical trials are needed to advance the field.

Design of a 3D-printed, open-source wrist-driven orthosis for individuals with spinal cord injury

Assistive technology, such as wrist-driven orthoses (WDOs), can be used by individuals with spinal cord injury to improve hand function. A lack of innovation and challenges in obtaining WDOs have limited their use. These orthoses can be heavy and uncomfortable for users and also time-consuming for orthotists to fabricate. The goal of this research was to design a WDO with user (N = 3) and orthotist (N = 6) feedback to improve the accessibility, customizability, and function of WDOs by harnessing advancements in 3D-printing. The 3D-printed WDO reduced hands-on assembly time to approximately 1.5 hours and the material costs to $15 compared to current fabrication methods. Varying improvements in users' hand function were observed during functional tests, such as the Jebsen Taylor Hand Function Test. For example, one participant's ability on the small object task improved by 29 seconds with the WDO, while another participant took 25 seconds longer to complete this task with the WDO. Two users had a significant increase in grasp strength with the WDO (13–122% increase), while the other participant was able to perform a pinching grasp for the first time. The WDO designs are available open-source to increase accessibility and encourage future innovation.

Corticomuscular coherence variation throughout the gait cycle during overground walking and ramp ascent: A preliminary investigation

Recent designs of neural-machine interfaces (NMIs) incorporating electroencephalography (EEG) or electromyography (EMG) have been used in lower limb assistive devices. While the results of previous studies have shown promise, a NMI which takes advantage of early movement-related EEG activity preceding movement onset, as well as the improved signal-to-noise ratio of EMG, could prove to be more accurate and responsive than current NMI designs based solely on EEG or EMG. Previous studies have demonstrated that the activity of the sensorimotor cortex is coupled to the firing rate of motor units in lower limb muscles during voluntary contraction. However, the exploration of corticomuscular coherence during locomotive tasks has been limited. In this study, coupling between the motor cortex and right tibialis anterior muscle activity was preliminarily investigated during self-paced over-ground walking and ramp ascent. EEG at the motor cortex and surface EMG from the tibialis anterior were collected from one able-bodied subject. Coherence between the two signals was computed and studied across gait cycles. The EEG activity led the EMG activity in the low gamma band in swing phase of level ground walking and in stance phase of ramp ascent. These results may inform the future design of EEG-EMG multimodal NMIs for lower limb devices that assist locomotion of people with physical disabilities.

Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals

With the development of Brain Machine Interface (BMI) systems, people with motor disabilities are able to control external devices to help them restore movement abilities. Longitudinal validation of these systems is critical not only to assess long-term performance reliability but also to investigate adaptations in electrocortical patterns due to learning to use the BMI system. In this paper, we decode the patterns of user's intended gait states (e.g., stop, walk, turn left, and turn right) from scalp electroencephalography (EEG) signals and simultaneously learn the relative importance of different brain areas by using the multiple kernel learning (MKL) algorithm. The region of importance (ROI) is identified during training the MKL for classification. The efficacy of the proposed method is validated by classifying different movement intentions from two subjects—an able-bodied and a spinal cord injury (SCI) subject. The preliminary results demonstrate that frontal and fronto-central regions are the most important regions for the tested subjects performing gait movements, which is consistent with the brain regions hypothesized to be involved in the control of lower-limb movements. However, we observed some regional changes comparing the able-bodied and the SCI subject. Moreover, in the longitudinal experiments, our findings exhibit the cortical plasticity triggered by the BMI use, as the classification accuracy and the weights for important regions—in sensor space—generally increased, as the user learned to control the exoskeleton for movement over multiple sessions.

Tolerance of neural decoding errors for powered artificial legs: A pilot study

Neural-machine interface (NMI) decoding errors challenge the clinical value of neural control of powered artificial legs, because these errors can dangerously disturb the user's walking balance, cause stumbles or falls, and thus threaten the user's confidence and safety in prosthesis use. Although extensive research efforts have been made to minimize the NMI decoding error rate, none of the current approaches can completely eliminate the errors in NMI. This study aimed at improving the robustness of prosthesis control system against neural decoding errors by introducing a fault-tolerant control (FTC) strategy. A novel reconfiguration mechanism, combined with our previously developed NMI decoding error detector, was designed and implemented into our prototypical powered knee prosthesis. The control system with FTC was preliminarily tested on two transfemoral amputees when they walked with the powered prosthesis on different walking terrains. Various NMI errors were simulated when the FTC was enabled and disabled. The preliminary testing results indicated that the FTC strategy was capable of effectively counteracting the disruptive effects of simulated decoding errors by reducing the mechanical work change around the prosthetic knee joint elicited by the NMI error. The outcomes of this study may provide a potential engineering solution to make the neural control of powered artificial legs safer to use.

Wireless sEMG-Based Body-Machine Interface for Assistive Technology Devices

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO.