Justin A. Brantley

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 exoskeletons 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.

Your Brain on Art: Emergent Cortical Dynamics During Aesthetic Experiences

The brain response to conceptual art was studied with mobile electroencephalography (EEG) to examine the neural basis of aesthetic experiences. In contrast to most studies of perceptual phenomena, participants were moving and thinking freely as they viewed the exhibit The Boundary of Life is Quietly Crossed by Dario Robleto at the Menil Collection-Houston. The brain activity of over 400 subjects was recorded using dry-electrode and one reference gel-based EEG systems over a period of 3 months. Here, we report initial findings based on the reference system. EEG segments corresponding to each art piece were grouped into one of three classes (complex, moderate, and baseline) based on analysis of a digital image of each piece. Time, frequency, and wavelet features extracted from EEG were used to classify patterns associated with viewing art, and ranked based on their relevance for classification. The maximum classification accuracy was 55% (chance = 33%) with delta and gamma features the most relevant for classification. Functional analysis revealed a significant increase in connection strength in localized brain networks while subjects viewed the most aesthetically pleasing art compared to viewing a blank wall. The direction of signal flow showed early recruitment of broad posterior areas followed by focal anterior activation. Significant differences in the strength of connections were also observed across age and gender. This work provides evidence that EEG, deployed on freely behaving subjects, can detect selective signal flow in neural networks, identify significant differences between subject groups, and report with greater-than-chance accuracy the complexity of a subjects visual percept of aesthetically pleasing art. Our approach, which allows acquisition of neural activity “in action and context,” could lead to understanding of how the brain integrates sensory input and its ongoing internal state to produce the phenomenon which we term aesthetic experience.

Noninvasive EEG correlates of overground and stair walking

Automated walking intention detection remains a challenge in lower-limb neuroprosthetic systems. Here, we assess the feasibility of extracting motor intent from scalp electroencephalography (EEG). First, we evaluated the corticomuscular coherence between central EEG electrodes (C1, Cz, C2) and muscles of the shank and thigh during walking on level ground and stairs. Second, we trained decoders to predict the linear envelope of the surface electromyogram (EMG). We observed significant EEG-led corticomuscular coupling between electrodes and sEMG (tibialis anterior) in the high delta (3-4 Hz) and low theta (4-5 Hz) frequency bands during level walking, indicating efferent signaling from the cortex to peripheral motor neurons. The coherence was increased between EEG and vastus lateralis and tibialis anterior in the delta band (<; 2 Hz) during stair ascent, indicating a task specific modulation in corticomuscular coupling. However, EMG was the leading signal for biceps femoris and gastrocnemius coherence during stair ascent, possibly representing afferent feedback loops from periphery to the motor cortex. Decoder validation showed that EEG signals contained information about the sEMG patterns during over ground walking, however, the accuracy of the predicted sEMG patterns decreased during the stair condition. Overall, these initial findings support the feasibility of integrating sEMG and EEG into a hybrid decoder for volitional control of lower limb neuroprostheses.

Electrocortical correlates of human level-ground, slope, and stair walking

This study investigated electrocortical dynamics of human walking across different unconstrained walking conditions (i.e., level ground (LW), ramp ascent (RA), and stair ascent (SA)). Non-invasive active-electrode scalp electroencephalography (EEG) signals were recorded and a systematic EEG processing method was implemented to reduce artifacts. Source localization combined with independent component analysis and k-means clustering revealed the involvement of four clusters in the brain during the walking tasks: Left and Right Occipital Lobe (LOL, ROL), Posterior Parietal Cortex (PPC), and Central Sensorimotor Cortex (SMC). Results showed that the changes of spectral power in the PPC and SMC clusters were associated with the level of motor task demands. Specifically, we observed α and β suppression at the beginning of the gait cycle in both SA and RA walking (relative to LW) in the SMC. Additionally, we observed significant β rebound (synchronization) at the initial swing phase of the gait cycle, which may be indicative of active cortical signaling involved in maintaining the current locomotor state. An increase of low γ band power in this cluster was also found in SA walking. In the PPC, the low γ band power increased with the level of task demands (from LW to RA and SA). Additionally, our results provide evidence that electrocortical amplitude modulations (relative to average gait cycle) are correlated with the level of difficulty in locomotion tasks. Specifically, the modulations in the PPC shifted to higher frequency bands when the subjects walked in RA and SA conditions. Moreover, low γ modulations in the central sensorimotor area were observed in the LW walking and shifted to lower frequency bands in RA and SA walking. These findings extend our understanding of cortical dynamics of human walking at different level of locomotion task demands and reinforces the growing body of literature supporting a shared-control paradigm between spinal and cortical networks during locomotion.

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.

Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground

Human locomotion is a complex process that requires the integration of central and peripheral nervous signalling. Understanding the brain’s involvement in locomotion is challenging and is traditionally investigated during locomotor imagination or observation. However, stationary imaging methods lack the ability to infer information about the peripheral and central signalling during actual task execution. In this report, we present a dataset containing simultaneously recorded electroencephalography (EEG), lower-limb electromyography (EMG), and full body motion capture recorded from ten able-bodied individuals. The subjects completed an average of twenty trials on an experimental gait course containing level-ground, ramps, and stairs. We recorded 60-channel EEG from the scalp and 4-channel EOG from the face and temples. Surface EMG was recorded from six muscle sites bilaterally on the thigh and shank. The motion capture system consisted of seventeen wireless IMUs, allowing for unconstrained ambulation in the experimental space. In this report, we present the rationale for collecting these data, a detailed explanation of the experimental setup, and a brief validation of the data quality.

Cortical features of locomotion-mode transitions via non-invasive EEG

This study investigates the neural features of locomotion mode transitions (i.e., level-ground walking to stair ascent) from non-invasive electroencephalography (EEG) signals. A systematic EEG processing method was implemented to reduce artifacts. Source localization using independent component analysis and k-mean clustering algorithm revealed the involvement of four clusters in the brain (Left and Right Occipital Lobe, Posterior Parietal Cortex, and Motor Cortex) during the walking tasks. Our results show significant differences in spectral power in the Occipital cluster between level-ground (LW) and stair (SA) walking. Additionally, significant increases in spectral power were detected up to 1.4 second before the critical transition time (LW to SA). The findings have implications for developing noninvasive lower-limb neuroprostheses that predict, rather than respond to, the user gait intentions. This work is a further step toward the development of a multimodal Neural-machine Interface (NMI) that fuses EEG and electromyography (EMG) signals for intuitive and flexible control of power prosthetic legs.

Prediction of lower-limb joint kinematics from surface EMG during overground locomotion

Recent advancements in powered lower limb prostheses have led to the development of neural-machine interfaces for natural control during bipedal locomotion. In particular, electromyography (EMG) patterns recorded from the amputated limb can be leveraged to infer the intended gait pattern of the user. However, the optimal control strategy for translating the EMG patterns to kinematic space remains a challenge. In this study, six able bodied subjects were instrumented for mobile brain-body imaging and asked to walk on a multi-terrain gait course. A non-linear extension of the Kalman filter was used to predict knee and ankle joint kinematics from lower limb muscle activation patterns during overground locomotion. Specifically, muscles of the anterior and posterior thigh were used to predict both the knee and ankle joint position. The results revealed that muscles in the thigh can be used to predict the position of the knee and ankle with high accuracy. The highest mean r-value obtained for each of the six subjects was 0.92, 0.77, 0.38, 0.39, 0.63, and 0.77, with corresponding SNR values of 10.8 dB, 6.7 dB, 5.9 dB, 2.8 dB, 9.1 dB, and 9.2 dB, for each subject, respectively. This study is the first to demonstrate that continuous EMG can be used to predict the joint kinematics of the knee and ankle during overground locomotion. This approach may provide improvements during closed-loop control of a powered lower limb prosthesis when compared to other pattern-recognition based methods.

Prediction of EMG envelopes of multiple terrains over-ground walking from EEG signals using an unscented Kalman filter

Advanced powered lower-limb prosthetic devices require an intuitive and flexible user control interface to work in a dynamic environment. This study investigated the feasibility of inferring muscle activation patterns (electromyography, EMG, envelope) from non-invasive electroencephalography (EEG) signals. Six healthy individuals participated in this study; the subjects were instructed to walk at a comfortable speed across various terrains (e.g. level-ground, up/down slope, up/down stair walking). An unscented kalman filter (UKF) was used to predict the EMG envelope from fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1–3 Hz). The highest decoding accuracy obtained was an r-value (Pearsons correlation r-value) of 0.57 in the medial gastrocnemius of a single subject. In the same subject, the mean r-value across all the muscle groups exceeded 0.4. The mean accuracy across all subjects and muscle group corresponded to an r-value of 0.236. As for the Signal to Noise Ratio (SNR), 79.3% of the obtained results were more than 0 dB with mean performance SNR of 0.8 (max: 2.8 to min: −1.7). The highest accuracy was obtained using a lag of 50ms with a window length (tap) of 500ms. In conclusion, this is the first study to show offline continuous decoding of the EMG envelope during over-ground walking on multiple terrains. The results show the feasibility of such neural decoding. This method could be coupled with EMG-based terrain prediction techniques to further improve the neural control interface with powered lower-limb prostheses.

Electrocortical amplitude modulations of human level-ground, slope, and stair walking

This study investigates if the electrocortical amplitude modulations relative to the mean gait cycle are different across walking conditions (i.e., level-ground (LW), ramp ascent (RA), and stair ascent (SA)). Non-invasive electroencephalography (EEG) signals were recorded and a systematic EEG processing method was implemented to reduce artifacts. Source localization using independent component analysis and k-means clustering revealed the involvement of four clusters in the brain (Left and Right Occipital Lobe, Posterior Parietal Cortex (PPC), and Sensorimotor Area) during the walking tasks. We found that electrocortical amplitude modulations varied across different walking conditions. Specifically, our results showed that the modulations in the PPC shifted to higher frequency bands when the subjects walked in RA and SA conditions. Moreover, we found low γ modulations in the sensorimotor area in LW walking and the modulations in this cluster shifted to lower frequency bands in RA and SA walking. These results are a promising step toward the development of a non-invasive Neural-machine Interface (NMI) for locomotion mode recognition.