A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling.

BACKGROUND: Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability. METHODS: We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis. We trained the participant's click detector using a small amount of training data (<44 min across 4 days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating. RESULTS: Using a click detector to navigate a switch scanning speller interface, the study participant can maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation can interrupt usage of a fixed model, a new click detector can achieve comparable performance despite being trained with even less data (<15 min, within 1 day). CONCLUSIONS: These results demonstrate that a click detector can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.

Amyotrophic lateral sclerosis (ALS) is a progressive disease of the nervous system that causes muscle weakness and leads to paralysis. People living with ALS therefore struggle to communicate with family and caregivers. We investigated whether the brain signals of a participant with ALS could be used to control a spelling application. Specifically, when the participant attempted a grasping movement, a computer method detected increased brain signals from electrodes implanted on the surface of his brain, and thereby generated a mouse-click. The participant clicked on letters or words from a spelling application to type sentences. Our method was trained using 44 min' worth of brain signals and performed reliably for three months without any retraining. This approach can potentially be used to restore communication to other severely paralyzed individuals over an extended time period and after only a short training period.

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling.

Background: Brain-computer interfaces (BCIs) can restore communication in movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command "click" decoders provide a basic yet highly functional capability. Methods: We sought to test the performance and long-term stability of click-decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis (ALS). We trained the participant's click decoder using a small amount of training data (< 44 minutes across four days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating. Results: Using this click decoder to navigate a switch-scanning spelling interface, the study participant was able to maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation interrupted testing with this fixed model, a new click decoder achieved comparable performance despite being trained with even less data (< 15 min, within one day). Conclusion: These results demonstrate that a click decoder can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.

Thermoplastic starch nanocomposites: sources, production and applications - a review.

The development of materials based on thermoplastic starch (TPS) is an excellent alternative to replace or reduce the use of petroleum-derived polymers. The abundance, renewable origin, biodegradability, biocompatibility, and low cost of starch are among the advantages related to the application of TPS compared to other thermoplastic biopolymers. However, through the literature review, it was possible to observe the need to improve some properties, to allow TPS to replace commonly used polyolefins. The studies reviewed achieved these modifications were achieved by using plasticizers, adjusting processing conditions, and incorporating fillers. In this sense, the addition of nanofillers proved to be the main modification strategy due to the large number of available nanofillers and the low charge concentration required for such improvement. The improvement can be seen in thermal, mechanical, electrical, optical, magnetic, antimicrobial, barrier, biocompatibility, cytotoxicity, solubility, and swelling properties. These modification strategies, the reviewed studies described the development of a wide range of materials. These are products with great potential for targeting different applications. Thus, this review addresses a wide range of essential aspects in developing of this type of nanocomposite. Covering from starch sources, processing routes, characterization methods, the properties of the obtained nanocomposites, to the various applications. Therefore, this review will provide an overview for everyone interested in working with TPS nanocomposites. Through a comprehensive review of the subject, which in most studies is done in a way directed to a specific area of study.

A Roadmap Towards Standards for Neurally Controlled End Effectors.

The control and manipulation of various types of end effectors such as powered exoskeletons, prostheses, and 'neural' cursors by brain-machine interface (BMI) systems has been the target of many research projects. A seamless "plug and play" interface between any BMI and end effector is desired, wherein similar user's intent cause similar end effectors to behave identically. This report is based on the outcomes of an IEEE Standards Association Industry Connections working group on End Effectors for Brain-Machine Interfacing that convened to identify and address gaps in the existing standards for BMI-based solutions with a focus on the end-effector component. A roadmap towards standardization of end effectors for BMI systems is discussed by identifying current device standards that are applicable for end effectors. While current standards address basic electrical and mechanical safety, and to some extent, performance requirements, several gaps exist pertaining to unified terminologies, data communication protocols, patient safety and risk mitigation.

A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface.

We present a mobile brain-body imaging (MoBI) dataset acquired during treadmill walking in a brain-computer interface (BCI) task. The data were collected from eight healthy subjects, each having three identical trials. Each trial consisted of three conditions: standing, treadmill walking, and treadmill walking with a closed-loop BCI. During the BCI condition, subjects used their brain activity to control a virtual avatar on a screen to walk in real-time. Robust procedures were designed to record lower limb joint angles (bilateral hip, knee, and ankle) using goniometers synchronized with 60-channel scalp electroencephalography (EEG). Additionally, electrooculogram (EOG), EEG electrodes impedance, and digitized EEG channel locations were acquired to aid artifact removal and EEG dipole-source localization. This dataset is unique in that it is the first published MoBI dataset recorded during walking. It is useful in addressing several important open research questions, such as how EEG is coupled with gait cycle during closed-loop BCI, how BCI influences neural activity during walking, and how a BCI decoder may be optimized.

Neural Decoding of Robot-Assisted Gait During Rehabilitation After Stroke.

OBJECTIVE: Advancements in robot-assisted gait rehabilitation and brain-machine interfaces may enhance stroke physiotherapy by engaging patients while providing information about robot-induced cortical adaptations. We investigate the feasibility of decoding walking from brain activity in stroke survivors during therapy using a powered exoskeleton integrated with an electroencephalography-based brain-machine interface. DESIGN: The H2 powered exoskeleton was designed for overground gait training with actuated hip, knee, and ankle joints. It was integrated with active-electrode electroencephalography and evaluated in hemiparetic stroke survivors for 12 sessions per 4 wks. A continuous-time Kalman decoder operating on delta-band electroencephalography was designed to estimate gait kinematics. RESULTS: Five chronic stroke patients completed the study with improvements in walking distance and speed training for 4 wks, correlating with increased offline decoding accuracy. Accuracies of predicted joint angles improved with session and gait speed, suggesting an improved neural representation for gait, and the feasibility to design an electroencephalography-based brain-machine interface to monitor brain activity or control a rehabilitative exoskeleton. CONCLUSIONS: The Kalman decoder showed increased accuracies as the longitudinal training intervention progressed in the stroke participants. These results demonstrate the feasibility of studying changes in patterns of neuroelectric cortical activity during poststroke rehabilitation and represent the first step in developing a brain-machine interface for controlling powered exoskeletons.

Multi-Trial Gait Adaptation of Healthy Individuals during Visual Kinematic Perturbations.

Optimizing rehabilitation strategies requires understanding the effects of contextual cues on adaptation learning. Prior studies have examined these effects on the specificity of split-belt walking adaptation, showing that contextual visual cues can be manipulated to modulate the magnitude, transfer, and washout of split-belt-induced learning in humans. Specifically, manipulating the availability of vision during training or testing phases of learning resulted in differences in adaptive mechanisms for temporal and spatial features of walking. However, multi-trial locomotor training has been rarely explored when using visual kinematic gait perturbations. In this study, we investigated multi-trial locomotor adaptation in ten healthy individuals while applying visual kinematic perturbations. Subjects were instructed to control a moving cursor, which represented the position of their heel, to follow a prescribed heel path profile displayed on a monitor. The perturbations were introduced by scaling all of the lower limb joint angles by a factor of 0.7 (i.e., a gain change), resulting in visual feedback errors between subjects' heel trajectories and the prescribed path profiles. Our findings suggest that, with practice, the subjects learned, albeit with different strategies, to reduce the tracking errors and showed faster response time in later trials. Moreover, the gait symmetry indices, in both the spatial and temporal domains, changed significantly during gait adaptation (P < 0.001). After-effects were present in the temporal gait symmetry index whens the visual perturbations were removed in the post-exposure period (P < 0.001), suggesting adaptation learning. These findings may have implications for developing novel gait rehabilitation interventions.

Unscented Kalman filter for neural decoding of human treadmill walking from non-invasive electroencephalography.

The feasibility of decoding lower limb kinematics in human treadmill walking from noninvasive electroencephalography (EEG) has been demonstrated with linear Wiener filter. However, nonlinear relationship between neural activities and limb movements may challenge the linear decoders in real-time brain computer interface (BCI) applications. In this study, we propose a nonlinear neural decoder using an Unscented Kalman Filter (UKF) to infer lower limb joint angles from noninvasive scalp EEG signals during human treadmill walking. Our results demonstrate that lower limb joint angles during treadmill walking can be decoded from the fluctuations in the amplitude of slow cortical potentials in the delta band (0.1-3Hz). Overall, the average decoding accuracy were 0.43 ± 0.18 for Pearson's r value and 1.82 ± 3.07 for signal to noise ratio (SNR), and robust to ocular, muscle, or movement artifacts. Moreover, the signal preprocessing scheme and the design of UKF allow the implementation of the proposed EEG-based BCI for real-time applications. This has implications for the development of closed-loop EEG-based BCI systems for gait rehabilitation after stroke.

Powered exoskeletons for bipedal locomotion after spinal cord injury.

OBJECTIVE: Powered exoskeletons promise to increase the quality of life of people with lower-body paralysis or weakened legs by assisting or restoring legged mobility while providing health benefits across multiple physiological systems. Here, a systematic review of the literature on powered exoskeletons addressed critical questions: What is the current evidence of clinical efficacy for lower-limb powered exoskeletons? What are the benefits and risks for individuals with spinal cord injury (SCI)? What are the levels of injury considered in such studies? What are their outcome measures? What are the opportunities for the next generation exoskeletons? APPROACH: A systematic search of online databases was performed to identify clinical trials and safety or efficacy studies with lower-limb powered exoskeletons for individuals with SCI. Twenty-two studies with eight powered exoskeletons thus selected, were analyzed based on the protocol design, subject demographics, study duration, and primary/secondary outcome measures for assessing exoskeleton's performance in SCI subjects. MAIN RESULTS: Findings show that the level of injury varies across studies, with T10 injuries being represented in 45.4% of the studies. A categorical breakdown of outcome measures revealed 63% of these measures were gait and ambulation related, followed by energy expenditure (16%), physiological improvements (13%), and usability and comfort (8%). Moreover, outcome measures varied across studies, and none had measures spanning every category, making comparisons difficult. SIGNIFICANCE: This review of the literature shows that a majority of current studies focus on thoracic level injury as well as there is an emphasis on ambulatory-related primary outcome measures. Future research should: 1) develop criteria for optimal selection and training of patients most likely to benefit from this technology, 2) design multimodal gait intention detection systems that engage and empower the user, 3) develop real-time monitoring and diagnostic capabilities, and 4) adopt comprehensive metrics for assessing safety, benefits, and usability.

Negligible Motion Artifacts in Scalp Electroencephalography (EEG) During Treadmill Walking.

Recent mobile brain/body imaging (MoBI) techniques based on active electrode scalp electroencephalogram (EEG) allow the acquisition and real-time analysis of brain dynamics during active unrestrained motor behavior involving whole body movements such as treadmill walking, over-ground walking and other locomotive and non-locomotive tasks. Unfortunately, MoBI protocols are prone to physiological and non-physiological artifacts, including motion artifacts that may contaminate the EEG recordings. A few attempts have been made to quantify these artifacts during locomotion tasks but with inconclusive results due in part to methodological pitfalls. In this paper, we investigate the potential contributions of motion artifacts in scalp EEG during treadmill walking at three different speeds (1.5, 3.0, and 4.5 km/h) using a wireless 64 channel active EEG system and a wireless inertial sensor attached to the subject's head. The experimental setup was designed according to good measurement practices using state-of-the-art commercially available instruments, and the measurements were analyzed using Fourier analysis and wavelet coherence approaches. Contrary to prior claims, the subjects' motion did not significantly affect their EEG during treadmill walking although precaution should be taken when gait speeds approach 4.5 km/h. Overall, these findings suggest how MoBI methods may be safely deployed in neural, cognitive, and rehabilitation engineering applications.

An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton.

Stroke remains a leading cause of disability, limiting independent ambulation in survivors, and consequently affecting quality of life (QOL). Recent technological advances in neural interfacing with robotic rehabilitation devices are promising in the context of gait rehabilitation. Here, the X1, NASA's powered robotic lower limb exoskeleton, is introduced as a potential diagnostic, assistive, and therapeutic tool for stroke rehabilitation. Additionally, the feasibility of decoding lower limb joint kinematics and kinetics during walking with the X1 from scalp electroencephalographic (EEG) signals--the first step towards the development of a brain-machine interface (BMI) system to the X1 exoskeleton--is demonstrated.