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

An experimental paradigm for studying sense of agency in joint human-machine motor actions

In this paper, we propose an experimental technique for studying the sense of agency (SoA) in joint human-machine actions. This technique is based on the use of an electromechanical finger-lifting device that enables a joint motor action initiated by a participant and completed by the machine. The joint action, later referred to as an "active-passive" action, was implemented as a reaction time task and contrasted with other levels of participant's involvement, including active movement, passive movement, and observation of a dummy's movement. In each trial, a feedback sound signal informed the participant whether they had performed the task successfully, i.e. faster than a threshold, which was individually adjusted in the beginning of the experiment. In the active condition, the result depended on the participant, while in other conditions it was preprogrammed for the servo. In context of this task, we studied direct time estimates made by participants and auditory event-related potentials (ERP) in 20 healthy volunteers. The amplitude of the auditory N1 component in the responses to the feedback sound showed no significant effect of activity and success factors, while its latency was shorter in successful trials. Interaction of activity and success factors was significant for subjective time estimates. Surprisingly, the intentional binding effect (subjective compression of time intervals, which is known as a correlate of SoA) only emerged in trials of active condition with negative results. This observation was in contrast with the fact that the active and active-passive movements were both voluntarily initiated by the participant. We believe that studying SoA with the proposed technique may not only add to the understanding of agency but also provide practically relevant results for the development of human-machine systems such as exoskeletons.

Assistance of a Person with Muscular Weakness Using a Joint-Torque-Assisting Exoskeletal Robot

Robotic systems for gait rehabilitation have been actively developed in recent years; many of the rehabilitation robots have been commercialized and utilized for treatment of real patients in hospitals. The first generation of gait rehabilitation robots was a tethered exoskeleton system on a treadmill. While these robots have become a new trend in rehabilitation medicine, there are several arguments about the effectiveness of such robots due to the passiveness of the motions that the robots generate, i.e., the continuous passive motions may limit the active involvement of patients’ voluntary motion control. In order to let a patient be more actively involved by requiring the self-control of whole-body balance, untethered powered exoskeletons, wearable robots that patients can wear and walk on the ground, are receiving great attention. While several powered exoskeletons have been commercialized already, the question about their effectiveness has not been cleared in the viewpoint of rehabilitation medicine because most of the powered exoskeletons provide still continuous passive motions, even though they are on the ground without tethering. This is due to their control strategy; the joints of a powered exoskeleton are position-controlled to repeatedly follow a predefined angle trajectory. This may be effective when a wearer is completely paraplegic such that the powered exoskeleton must generate full actuation power for walking. For people with muscular weakness due to various reasons, the powered exoskeleton must assist only the lack of muscular force without constraining human motion. For assistance and rehabilitation of people with partial impairment in walking ability, Angel Legs is introduced in this paper. The proposed powered exoskeleton system is equipped with a transparent actuation system such that the assistive force is accurately generated. The overall design and control of Angel Legs are introduced in this paper, and a clinical verification with a human subject is also provided.

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain-Machine Interface (BMI)

Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain-machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier-support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.

Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton

This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

BACKGROUND

Non-invasive brain-computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding, which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography, 7-channel electromyography, and 4-channel electro-oculography of 25 healthy participants collected over 3-day sessions for a total of 82,500 trials across all the participants.

FINDINGS

We validated our dataset via neurophysiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery, respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method.

CONCLUSIONS

The dataset includes the data of multiple recording sessions, various classes within the single upper extremity, and multimodal signals. This work can be used to (i) compare the brain activities associated with real movement and imagination, (ii) improve the decoding performance, and (iii) analyze the differences among recording sessions. Hence, this study, as a Data Note, has focused on collecting data required for further advances in the BCI technology.

Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton

Brain-machine interfaces (BMIs) can improve the control of assistance mobility devices making its use more intuitive and natural. In the case of an exoskeleton, they can also help rehabilitation therapies due to the reinforcement of neuro-plasticity through repetitive motor actions and cognitive engagement of the subject. Therefore, the cognitive implication of the user is a key aspect in BMI applications, and it is important to assure that the mental task correlates with the actual motor action. However, the process of walking is usually an autonomous mental task that requires a minimal conscious effort. Consequently, a brain-machine interface focused on the attention to gait could facilitate sensory integration in individuals with neurological impairment through the analysis of voluntary gait will and its repetitive use. This way the combined use of BMI+exoskeleton turns from assistance to restoration. This paper presents a new brain-machine interface based on the decoding of gamma band activity and attention level during motor imagery mental tasks. This work also shows a case study tested in able-bodied subjects prior to a future clinical study, demonstrating that a BMI based on gamma band and attention-level paradigm allows real-time closed-loop control of a Rex exoskeleton.

Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study

The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some brain rhythms experience during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimation using the Fourier analysis. This paper explores the decomposition of the brain rhythms based on the Empirical Mode Decomposition, as an alternative for the analysis of electroencephalographic (EEG) signals, due to its adaptive capability to the local oscillations of the data, showcasing it as a viable tool for future BMI algorithms based on motor related events.

Passive, yet not inactive: robotic exoskeleton walking increases cortical activation dependent on task

Background

Experimental designs using surrogate gait-like movements, such as in functional magnetic resonance imaging (MRI), cannot fully capture the cortical activation associated with overground gait. Overground gait in a robotic exoskeleton may be an ideal tool to generate controlled sensorimotor stimulation of gait conditions like ‘active’ (i.e. user moves with the device) and ‘passive’ (i.e. user is moved by the device) gait. To truly understand these neural mechanisms, functional near-infrared spectroscopy (fNIRS) would yield greater ecological validity. Thus, the aim of this experiment was to use fNIRS to delineate brain activation differences between ‘Active’ and ‘Passive’ overground gait in a robotic exoskeleton.

Methods

Fourteen healthy adults performed 10 walking trials in a robotic exoskeleton for Passive and Active conditions, with fNIRS over bilateral frontal and parietal lobes, and electromyography (EMG) over bilateral thigh muscles. Digitization of optode locations and individual T1 MRI scans were used to demarcate the brain regions fNIRS recorded from.

Results

Increased oxyhemoglobin in the right frontal cortex was found for Passive compared with Active conditions. For deoxyhemoglobin, increased activation during Passive was found in the left frontal cortex and bilateral parietal cortices compared with Active; one channel in the left parietal cortex decreased during Active when compared with Passive. Normalized EMG mean amplitude was higher in the Active compared with Passive conditions for all four muscles (p ≤ 0.044), confirming participants produced the conditions asked of them.

Conclusions

The parietal cortex is active during passive robotic exoskeleton gait, a novel finding as research to date has not recorded posterior to the primary somatosensory cortex. Increased activation of the parietal cortex may be related to the planning of limb coordination while maintaining postural control. Future neurorehabilitation research could use fNIRS to examine whether exoskeletal gait training can increase gait-related brain activation with individuals unable to walk independently.

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

Background: Stroke is a disease with a high associated disability burden. Robotic-assisted gait training offers an opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG), or electromyography (EMG) can offer new strategies for robotic gait re-education after a stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and the limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8,899 papers identified, 13 articles were considered for the final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait, two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG-related gait data, no EEG study has been identified to close the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle, and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualized robot-driven step trajectory. Conclusions: Wide heterogeneity in the reporting and the purpose of neurobiosignal use during robotic gait training after a stroke exists. Neural interfacing with robotic gait after a stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after a stroke would benefit from a more standardized protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to the study type and the participants' characteristics.

Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural Networks

The reliable classification of Electroencephalography (EEG) signals is a crucial step towards making EEG-controlled non-invasive neuro-exoskeleton rehabilitation a practical reality. EEG signals collected during motor imagery tasks have been proposed to act as a control signal for exoskeleton applications. Here, a Deep Convolutional Neural Network (DCNN) was optimized to classify a two-class kinesthetic motor imagery EEG dataset, leading to an optimized architecture consisting of four convolutional layers and three fully connected layers. Transfer learning, or the leveraging of data from past subjects to classify the intentions of a new subject, is important for rehabilitation as it helps to minimize the number of training sessions required from subjects who lack full motor functionality. The transfer learning training paradigm investigated through this study utilized region criticality trends to reduce the number of new subject training sessions and increase the classification performance when compared against a single-subject non-transfer-learning classifier.

Method for positioning and rehabilitation training with the ExoAtlet ® powered exoskeleton

Exoskeletons for locomotion, support, or other uses are becoming more common. An increasing number of studies are demonstrating relevant results in rehabilitation. Here we describe the steps required to properly place and train patients in ExoAtlet ® powered exoskeletons (Moscow, Russia), for which there is currently limited information available. These steps combine actions related to the hardware, software, as well as safety, rehabilitation, and psycho-emotional state of the subject. Training starts with a general preparation of the environment, the equipment, and the patient. When the actual training program begins, the patient needs to gradually learn to perform the different actions that will be required to control the exoskeleton. Initially, training requires transferring weight between legs to guarantee adequate equilibrium control. Then, actions assisted by computer-controlled motors begin, namely: standing up, walking in place, moving small distances and sitting down. As the patient becomes comfortable with the exoskeleton and the cardiovascular system becomes adjusted to the upright position, training can then include walking over longer distances, inclined planes, opening doors, and climbing stairs.•Powered exoskeletons are becoming a common method in rehabilitation.•The use of ExoAtlet ® powered exoskeletons in clinical research requires manipulation of variables thought to promote rehabilitation, without compromising safety standards.•The phases of training are: transferring weight between legs, walk in place, and walk over longer distances.

Mu-Beta event-related (de)synchronization and EEG classification of left-right foot dorsiflexion kinaesthetic motor imagery for BCI

The left and right foot representation area is located within the interhemispheric fissure of the sensorimotor cortex and share spatial proximity. This makes it difficult to visualize the cortical lateralization of event-related (de)synchronization (ERD/ERS) during left and right foot motor imageries. The aim of this study is to investigate the possibility of using ERD/ERS in the mu, low beta, and high beta bandwidth, during left and right foot dorsiflexion kinaesthetic motor imageries (KMI), as unilateral control commands for a brain-computer interface (BCI). EEG was recorded from nine healthy participants during cue-based left-right foot dorsiflexion KMI tasks. The features were analysed for common average and bipolar references. With each reference, mu and beta band-power features were analysed using time–frequency (TF) maps, scalp topographies, and average time course for ERD/ERS. The cortical lateralization of ERD/ERS, during left and right foot KMI, was confirmed. Statistically significant features were classified using LDA, SVM, and KNN model, and evaluated using the area under ROC curves. An increase in high beta power following the end of KMI for both tasks was recorded, from right and left hemispheres, respectively, at the vertex. The single trial analysis and classification models resulted in high discrimination accuracies, i.e. maximum 83.4% for beta ERS, 79.1% for beta ERD, and 74.0% for mu ERD. With each model the features performed above the statistical chance level of 2-class discrimination for a BCI. Our findings indicate these features can evoke left-right differences in single EEG trials. This suggests that any BCI employing unilateral foot KMI can attain classification accuracy suitable for practical implementation. Given results stipulate the novel utilization of mu and beta as independent control features for discrimination of bilateral foot KMI in a BCI.

Effects of an Exoskeleton-Assisted Gait Motor Imagery Training in Functional Brain Connectivity

Lower-limb exoskeletons have been used in gait rehabilitation to facilitate the restoration of motor skills. These robotics systems could be complemented by Brain-Computer Interfaces (BCIs) to assist or rehabilitate people with walking disabilities. In this preliminary study, electroencephalography-based brain functional connectivity is analyzed during exoskeleton-assisted gait motor imagery (MI) training. Partial Directed Coherence (PDC) analysis was employed to assess the exchange of information flow between EEG signals during gait MI in four healthy subjects, two using an exoskeleton and two without using it. Besides, in order to explore the functional connectivity, an outflow index based on the number of significant directed connectivities revealed by the PDC analysis is proposed. We found that the outflow index increases in the central zone (C2, C3, C4) while decreases in the central-parietal (CP1, CP2) and fronto-central (FC1) zones when the training was assisted by an exoskeleton. The results obtained can be useful to obtain informative features for BCI applications as well as in motor rehabilitation.

Cortical Oscillations during Gait: Wouldn't Walking be so Automatic?

Gait is often considered as an automatic movement but cortical control seems necessary to adapt gait pattern with environmental constraints. In order to study cortical activity during real locomotion, electroencephalography (EEG) appears to be particularly appropriate. It is now possible to record changes in cortical neural synchronization/desynchronization during gait. Studying gait initiation is also of particular interest because it implies motor and cognitive cortical control to adequately perform a step. Time-frequency analysis enables to study induced changes in EEG activity in different frequency bands. Such analysis reflects cortical activity implied in stabilized gait control but also in more challenging tasks (obstacle crossing, changes in speed, dual tasks…). These spectral patterns are directly influenced by the walking context but, when analyzing gait with a more demanding attentional task, cortical areas other than the sensorimotor cortex (prefrontal, posterior parietal cortex, etc.) seem specifically implied. While the muscular activity of legs and cortical activity are coupled, the precise role of the motor cortex to control the level of muscular contraction according to the gait task remains debated. The decoding of this brain activity is a necessary step to build valid brain-computer interfaces able to generate gait artificially.

Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback

Brain computer interface (BCI) adopts human brain signals to control external devices directly without using normal neural pathway. Recent study has explored many applications, such as controlling a teleoperation robot by electroencephalography (EEG) signals. However, utilizing the motor imagery EEG-based BCI to perform teleoperation for reach and grasp task still has many difficulties, especially in continuous multidimensional control of robot and tactile feedback. In this research, a motor imagery EEG-based continuous teleoperation robot control system with tactile feedback was proposed. Firstly, mental imagination of different hand movements was translated into continuous command to control the remote robotic arm to reach the hover area of the target through a wireless local area network (LAN). Then, the robotic arm automatically completed the task of grasping the target. Meanwhile, the tactile information of remote robotic gripper was detected and converted to the feedback command. Finally, the vibrotactile stimulus was supplied to users to improve their telepresence. Experimental results demonstrate the feasibility of using the motor imagery EEG acquired by wireless portable equipment to realize the continuous teleoperation robot control system to finish the reach and grasp task. The average two-dimensional continuous control success rates for online Task 1 and Task 2 of the six subjects were 78.0% ± 6.1% and 66.2% ± 6.0%, respectively. Furthermore, compared with the traditional EEG triggered robot control using the predefined trajectory, the continuous fully two-dimensional control can not only improve the teleoperation robot system’s efficiency but also give the subject a more natural control which is critical to human–machine interaction (HMI). In addition, vibrotactile stimulus can improve the operator’s telepresence and task performance.

Decoding Movement-Related Cortical Potentials Based on Subject-Dependent and Section-Wise Spectral Filtering

An important challenge in developing a movement-related cortical potential (MRCP)-based brain-machine interface (BMI) is an accurate decoding of the user intention for real-world environments. However, the performance remains insufficient for real-time decoding owing to the endogenous signal characteristics compared to other BMI paradigms. This study aims to enhance the MRCP decoding performance from the perspective of preprocessing techniques (i.e., spectral filtering). To the best of our knowledge, existing MRCP studies have used spectral filters with a fixed frequency bandwidth for all subjects. Hence, we propose a subject-dependent and section-wise spectral filtering (SSSF) method that considers the subjects' individual MRCP characteristics for two different temporal sections. In this study, MRCP data were acquired under a powered exoskeleton environments in which the subjects conducted self-initiated walking. We evaluated our method using both our experimental data and a public dataset (BNCI Horizon 2020). The decoding performance using the SSSF was 0.86 (±0.09), and the performance on the public dataset was 0.73 (±0.06) across all subjects. The experimental results showed a statistically significant enhancement ( ) compared with the fixed frequency bands used in previous methods on both datasets. In addition, we presented successful decoding results from a pseudo-online analysis. Therefore, we demonstrated that the proposed SSSF method can involve more meaningful MRCP information than conventional methods.

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.

Pseudo-Online BMI Based on EEG to Detect the Appearance of Sudden Obstacles during Walking

The aim of this paper is to describe new methods for detecting the appearance of unexpected obstacles during normal gait from EEG signals, improving the accuracy and reducing the false positive rate obtained in previous studies. This way, an exoskeleton for rehabilitation or assistance of people with motor limitations commanded by a Brain-Machine Interface (BMI) could be stopped in case that an obstacle suddenly appears during walking. The EEG data of nine healthy subjects were collected during their normal gait while an obstacle appearance was simulated by the projection of a laser line in a random pattern. Different approaches were considered for selecting the parameters of the BMI: subsets of electrodes, time windows and classifier probabilities, which were based on a linear discriminant analysis (LDA). The pseudo-online results of the BMI for detecting the appearance of obstacles, with an average percentage of 63.9% of accuracy and 2.6 false positives per minute, showed a significant improvement over previous studies.

Perspectives and Challenges in Robotic Neurorehabilitation

The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots’ capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients’ recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.

Characterizing Intent Changes in Exoskeleton-Assisted Walking Through Onboard Sensors

Robotic exoskeletons are a promising technology for rehabilitation and locomotion following musculoskeletal injury, but their adoption outside the physical therapy clinic has been limited by relatively primitive methods for identifying and incorporating the user's gait intentions. Various intent detection approaches have been demonstrated using electromyography and electroencephalography signals. These technologies sense the human directly but introduce complications for donning/doffing the device and in measurement consistency. By contrast, sensors onboard the exoskeleton avoid these complications but sense the human indirectly via the human-robot interface. This pilot study examines if onboard sensors alone may enable identification of user intent. Joint positions and commanded motor currents are compared prior to and after changes in the user's intended gait speed. Preliminary experimental results confirm that these measures are significantly different following intent changes for both able-bodied and non-able-bodied users. The findings suggest that intent detection is possible with onboard sensors alone, but the intent signals depend on exoskeleton control settings, user ability, and temporal considerations.

Trends in research participant categories and descriptions in abstracts from the International BCI Meeting series, 1999 to 2016

Much brain-computer interface (BCI) research is intended to benefit people with disabilities (PWD), but inclusion of these individuals as study participants remains relatively rare. When participants with disabilities are included, they are described with a range of clinical and non-clinical terms with varying degrees of specificity, often leading to difficulty in interpreting or replicating results. This study examined trends in inclusion and description of study participants with disabilities across six International BCI Meetings from 1999 to 2016. Abstracts from each Meeting were analyzed by two trained independent reviewers. Results suggested a decline in participation by PWD across Meetings until the 2016 Meeting. Increased diagnostic specificity was noted at the 2013 and 2016 Meetings. Fifty-eight percent of the abstracts identified PWD as being the target beneficiaries of BCI research, though only twenty-two percent included participants with disabilities, suggesting evidence of a persistent translational gap. Participants with disabilities were most commonly described as having physical and/or communication impairments compared to impairments in other areas. Implementing participatory action research principles and user-centered design strategies continues to be necessary within BCI research to bridge the translational gap and facilitate use of BCI systems within functional environments for PWD.

Detection of Driver Braking Intention Using EEG Signals During Simulated Driving

In this work, we developed a novel system to detect the braking intention of drivers in emergency situations using electroencephalogram (EEG) signals. The system acquired eight-channel EEG and motion-sensing data from a custom-designed EEG headset during simulated driving. A novel method for accurately labeling the training data during an extremely short period after the onset of an emergency stimulus was introduced. Two types of features, including EEG band power-based and autoregressive (AR)-based, were investigated. It turned out that the AR-based feature in combination with artificial neural network classifier provided better detection accuracy of the system. Experimental results for ten subjects indicated that the proposed system could detect the emergency braking intention approximately 600 ms before the onset of the executed braking event, with high accuracy of 91%. Thus, the proposed system demonstrated the feasibility of developing a brain-controlled vehicle for real-world applications.

Controlling a Lower-Leg Exoskeleton Using Voltage and Current Variation Signals of a DC Motor Mounted at the Knee Joint

Powered exoskeleton technology helps turns dreams of recovering mobility after paralysis into reality. One of the most common problems encountered in the use of powered exoskeletons is the detection of the motion intentions of the user. Many approaches to conquering this problem have been developed using Electromyography (EMG) sensors, Electroencephalography (EEG) sensors, Center of Pressure (COP), and so forth. When a method, such as the surface EMG, is contaminated with noise during acquisition, it is important to process that raw EMG signal. Doing so usually takes time, and time delays in such a system can lead to a loss in synchronization between the wearer and the exoskeleton. Many algorithms have been developed for data acquisition and the filtering of raw EMG signals as well as accelerometer data. Our approach involves designing an almost sensor-less low limb exoskeleton that is powered by an electric Direct Current (DC) motor, and the same motor is used to detect motion via monitoring the voltage and the current variation. Experimental results are obtained for the actuating knee flexion-to-extension then extension-to-flexion of a sitting person using the National Instrument (NI) MyRIO as a data acquisition system with NI-LabView. The results support the hypothesis that the developed system can detect human motion and drive the motor in the necessary direction without the use of uncomfortable electrodes (sensors) and their connections. Additionally, the system supported the wearer to move his leg up (extension) without having too much effort to do so. In order to identify muscle activation with the change in the angle along the sagittal plane, an accelerometer has been attached to the system. The proposed approach could help open a new pathway along which researchers could develop low-cost and easy-to-wear powered exoskeletons which could emulate precisely the normal gait of a human.

Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations

Brain-machine interfaces (BMIs) have exploded in popularity in the past decade. BMIs, also called brain-computer interfaces, provide a direct link between the brain and a computer, usually to control an external device. BMIs have a wide array of potential clinical applications, ranging from restoring communication to people unable to speak due to amyotrophic lateral sclerosis or a stroke, to restoring movement to people with paralysis from spinal cord injury or motor neuron disease, to restoring memory to people with cognitive impairment. Because BMIs are controlled directly by the activity of prespecified neurons or cortical areas, they also provide a powerful paradigm with which to investigate fundamental questions about brain physiology, including neuronal behavior, learning, and the role of oscillations. This article reviews the clinical and neuroscientific applications of BMIs, with a primary focus on motor BMIs.

EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review

Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices is still in early stages. Although the EEG-based control of assistive devices has attracted a considerable level of attention over the last few years, few studies have been carried out to systematically review these studies, as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used for assistive technology. Therefore, this research has three main goals. The first aim is to systematically gather, summarize, evaluate and synthesize information regarding the accuracy and the value of previous research published in the literature between 2011 and 2018. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control for assistive rehabilitation devices to all the experts and scientists. The third goal is to recognize the gap of knowledge that demands further investigation and to recommend directions for future research in this area.

EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots

Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user's mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications' key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It is suggested to structure EEG-BCI controlled LL assistive devices within the presented framework, for future generation of intent-based multifunctional controllers. Despite the development of controllers, for BCI-based wearable or assistive devices that can seamlessly integrate user intent, practical challenges associated with such systems exist and have been discerned, which can be constructive for future developments in the field.