Reduced Proactive and Reactive Cognitive Flexibility in Older Adults Underlies Performance Costs During Dual-Task Walking: A Mobile Brain/Body Imaging (MoBI) Study

Age-related reductions in cognitive flexibility may limit modulation of control processes during systematic increases to cognitive-motor demands, exacerbating dual-task costs. In this study, behavioral and neurophysiologic changes to proactive and reactive control during progressive cognitive-motor demands were compared across older and younger adults to explore the basis for age-differences in cognitive-motor interference (CMI). 19 younger (19 - 29 years old, mean age = 22.84 +/- 2.75 years, 6 male, 13 female) and 18 older (60 - 77 years old, mean age = 67.89 +/- 4.60 years, 9 male, 9 female) healthy adults completed cued task-switching while alternating between sitting and walking on a treadmill. Gait kinematics, task performance measures, and brain activity were recorded using electroencephalography (EEG) based Mobile Brain/Body Imaging (MoBI). Response accuracy on easier trial types improved in younger, but not older adults when they walked while performing the cognitive task. As difficulty increased, walking provoked accuracy costs in older, but not younger adults. Both groups registered faster responses and reduced gait variability during dual-task walking. Older adults exhibited lower amplitude modulations of proactive and reactive neural activity as cognitive-motor demands systematically increased, which may reflect reduced flexibility for progressive preparatory and reactive adjustments over behavioral control.

Intramuscular and intermuscular coherence analysis while obstacle crossing during treadmill gait

PURPOSE

This study aimed to identify the contribution of the common synaptic drives to motor units during obstacle avoidance, using coherence analysis between a-pair electromyography (EMG) signals (EMG-EMG coherence).

MATERIALS AND METHODS

Fourteen healthy volunteers walked on a treadmill with and without obstacle avoidance. During obstacle gait, subjects were instructed to step over an obstacle with their right leg while walking that would randomly and unpredictably appear. Surface EMG signals were recorded from the following muscles of the right leg: the proximal and distal ends of tibialis anterior (TAp and TAd), biceps femoris (BF), semitendinosus (ST), lateral gastrocnemius (LG), and medial gastrocnemius (MG). Beta-band (13-30 Hz) EMG-EMG coherence was analysed.

RESULTS

Beta-band EMG-EMG coherence of TAp-TAd during swing phase and BF-ST during pre and initial swing phase when stepping over an obstacle were significantly higher compared to normal gait (both p < 0.05). Beta-band EMG-EMG coherence of TAp-TAd, BF-ST, and LG-MG during stance phase were not significantly different between the two gait conditions (all p > 0.05).

CONCLUSIONS

The present findings suggest increased common synaptic drives to motor units in ankle dorsiflexor and knee flexor muscles during obstacle avoidance. It also may reflect an increased cortical contribution to modify the gait patterns to avoid an obstacle.

Ankle Strategies for Step-Aside Movement during Straight Walking

The step-aside movement, also known as the dodging step, is a common maneuver for avoiding obstacles while walking. However, differences in neural control mechanisms and ankle strategies compared to straight walking can pose a risk of falling. This study aimed to examine the differences in tibialis anterior (TA), peroneus longus (PL), and soleus (SOL) muscle contractions, foot center of pressure (CoP) displacement, and ground reaction force (GRF) generation between step-aside movement and straight walking to understand the mechanism behind step-aside movement during walking. Twenty healthy young male participants performed straight walking and step-aside movements at comfortable walking speeds. The participants' muscle contractions, CoP displacement, and GRF were measured. The results show significant greater bilateral ankle muscle contractions during the push and loading phases of step-aside movement than during straight walking. Moreover, the CoP displacement, GRF generation mechanism, and timing differed from those observed during straight walking. These findings provide valuable insights for rehabilitation professionals in the development of clinical decisions for populations at a risk of falls and lacking gait stability.

Brain Structural Correlates of Obstacle Negotiation in Mild Cognitive Impairment: Results from the Gait and Brain Study

INTRODUCTION

Mild cognitive impairment (MCI) affects obstacle negotiation capabilities, potentially increasing the risk of falls in older adults. However, it is unclear whether smaller brain volumes typically observed in older individuals with MCI are related to the observed hazardous obstacle negotiation in this population.

METHODS

A total of 93 participants (71.9 ± 5.36 years of age; MCI = 53/control = 40) from the Gait and Brain Study were analyzed. Gray matter (GM) volumes from the frontal, temporal, and parietal lobes were entered in the analysis. Gait performance was recorded using a 6-m electronic walkway during two cognitive load conditions while approaching and stepping over an obstacle: (1) single-task and (2) while counting backwards by 1s from 100 (dual-task). Anticipatory adjustments in gait performance to cross an "ad hoc" obstacle were electronically measured during pre-crossing phases: early (3 steps before the late phase) and late (3 steps before obstacle). Association between the percentage of change in average gait speed and step length from early to late (i.e., anticipatory adjustments) and GM volumes was investigated using multivariate models adjusted for potential confounders.

RESULTS

Anticipatory adjustments in gait speed (Wilks' lambda: 0.35; Eta2: 0.64; p = 0.01) and step length (Wilks' lambda: 0.33; Eta2: 0.66; p = 0.01) during dual-task conditions were globally associated with GM volumes in MCI. Individuals with MCI with smaller GM volumes in the left inferior frontal gyrus, left hippocampus, right hippocampus, and right entorhinal cortex made significantly fewer anticipatory gait adjustments prior to crossing the obstacle.

CONCLUSION

Frontotemporal atrophy may affect obstacle negotiation capabilities potentially increasing the risk of falls in MCI.

Effects of proprioceptive neuromuscular facilitation stretching in relieving pain and balancing knee loading during stepping over obstacles among older adults with knee osteoarthritis: A randomized controlled trial

OBJECTIVE

The purpose of this study was to investigate the effects of an 8-week proprioceptive neuromuscular facilitation (PNF) stretching in relieving pain and balancing knee loading during stepping over obstacles among older people with knee osteoarthritis, and further explore the improvements in gait patterns.

DESIGN

Thirty-two older adults (66~72 years) with KOA were recruited and randomly assigned into PNF or control groups. They received PNF stretching or health lecture series for 8 weeks. Final data analyses were conducted among 13 participants in the PNF and 14 in the control groups. At weeks 0 and 9, they were asked to step over an obstacle of 20% of their leg length. The pain scores and knee abduction moment (KAM) (primary outcomes) were analyzed by multivariate ANOVA, and the gait variables (secondary outcomes) were analyzed by two-way (group by pre-/post) ANOVAs with repeated measures.

RESULTS

Significant interactions were detected in the pain score, first and second peaks of KAM, and crossing velocity during stepping over obstacles, and significant between-group differences of these outcomes were detected at week 9.

CONCLUSION

An 8-week PNF stretching could relieve pain and balance loading between knee compartments, as well as increase crossing velocity during stepping over obstacles.

TRIAL REGISTRATION

Chinese Clinical Trial Registry: ChiCTR2100042278.

Kinematic strategies for obstacle-crossing in older adults with mild cognitive impairment

Introduction

Mild cognitive impairment (MCI) is considered a transitional stage between soundness of mind and dementia, often involving problems with memory, which may lead to abnormal postural control and altered end-point control when dealing with neuromechanical challenges during obstacle-crossing. The study aimed to identify the end-point control and angular kinematics of the pelvis-leg apparatus while crossing obstacles for both leading and trailing limbs.

Methods

12 patients with MCI (age: 66.7 ± 4.2 y/o; height: 161.3 ± 7.3 cm; mass: 62.0 ± 13.6 kg) and 12 healthy adults (age: 67.7 ± 2.9 y/o; height: 159.3 ± 6.1 cm; mass: 61.2 ± 12.0 kg) each walked and crossed obstacles of three different heights (10, 20, and 30% of leg length). Angular motions of the pelvis and lower limbs and toe-obstacle clearances during leading- and trailing-limb crossings were calculated. Two-way analyses of variance were used to study between-subject (group) and within-subject (obstacle height) effects on the variables. Whenever a height effect was found, a polynomial test was used to determine the trend. A significance level of α = 0.05 was set for all tests.

Results

Patients with MCI significantly increased pelvic anterior tilt, hip abduction, and knee adduction in the swing limb during leading-limb crossing when compared to controls (p < 0.05). During trailing-limb crossing, the MCI group showed significantly decreased pelvic posterior tilt, as well as ankle dorsiflexion in the trailing swing limb (p < 0.05).

Conclusion

Patients with MCI adopt altered kinematic strategies for successful obstacle-crossing. The patients were able to maintain normal leading and trailing toe-obstacle clearances for all tested obstacle heights with a specific kinematic strategy, namely increased pelvic anterior tilt, swing hip abduction, and knee adduction during leading-limb crossing, and decreased pelvic posterior tilt and swing ankle dorsiflexion during trailing-limb crossing. The current results suggest that regular monitoring of obstacle-crossing kinematics for reduced toe-obstacle clearance or any signs of changes in crossing strategy may be helpful for early detection of compromised obstacle-crossing ability in patients with single-domain amnestic MCI. Further studies using a motor/cognitive dual-task approach on the kinematic strategies adopted by multiple-domain MCI will be needed for a complete picture of the functional adaptations in such a patient group.

Effects of multi-obstacle contexts on obstacle negotiation strategies in healthy older adults under dual-task conditions

BACKGROUND

Performance of obstacle crossing is an attentionally demanding task due to the need for motor planning and gait regulation, particularly among older adults. Despite extensive studies on age-associated changes in obstacle negotiation strategies, relatively little is known about adaptive mechanisms in the elderly regarding multiple obstacle crossings with different execution demands.

RESEARCH QUESTION

For better understanding of avoidance strategies employed by the elderly, the current study investigated adaptive mechanisms related to planning and implementation of more complex multi-obstacle contexts. Do older adults use a more conservative strategy such as prolonged step duration or elevated foot height when crossing obstacles with increased task demands of obstacle negotiation?

METHODS

Eleven healthy older and 11 young adults participated in the experiment. We examined how the presence and physical property of the second obstacle influenced the planning and adjustments for obstacle avoidance performance. Spatiotemporal characteristics of the stepping movement were analyzed using a 3D motion capture system.

RESULTS

Older adults showed a longer stance time before crossing the first obstacle than young adults when the task complexity increased. These stepping characteristics were more evident in the dual-task condition. However, their foot clearance and crossing speed were not influenced by the level of task complexity.

SIGNIFICANCE

These findings suggest that healthy elderly participants may have difficulty in developing the motor plan rather than implementing the stepping strategies under more complex obstacle constraints. A general cognitive decline with advancing age or adaptation of compensatory adjustment to enhance postural stability may underlie such altered obstacle negotiation behaviors in older adults.

Enhancing Locomotor Learning With Transcutaneous Spinal Electrical Stimulation and Somatosensory Augmentation: A Pilot Randomized Controlled Trial in Older Adults

This study investigated locomotor learning of a complex terrain walking task in older adults, when combined with two adjuvant interventions: transcutaneous spinal direct current stimulation (tsDCS) to increase lumbar spinal cord excitability, and textured shoe insoles to increase somatosensory feedback to the spinal cord. The spinal cord has a crucial contribution to control of walking, and is a novel therapeutic target for rehabilitation of older adults. The complex terrain task involved walking a 10-meter course consisting of nine obstacles and three sections of compliant (soft) walking surface. Twenty-three participants were randomly assigned to one of the following groups: sham tsDCS and smooth insoles (sham/smooth; control group), sham tsDCS and textured insoles (sham/textured), active tsDCS and smooth insoles (active/smooth), and active tsDCS and textured insoles (active/textured). The first objective was to assess the feasibility, tolerability, and safety of the interventions. The second objective was to assess preliminary efficacy for increasing locomotor learning, as defined by retention of gains in walking speed between a baseline visit of task practice, and a subsequent follow-up visit. Variability of the center of mass while walking over the course was also evaluated. The change in executive control of walking (prefrontal cortical activity) between the baseline and follow-up visits was measured with functional near infrared spectroscopy. The study results demonstrated feasibility based on enrollment and retention of participants, tolerability based on self-report, and safety based on absence of adverse events. Preliminary efficacy was supported based on trends showing larger gains in walking speed and more pronounced reductions in mediolateral center of mass variability at the follow-up visit in the groups randomized to active tsDCS or textured insoles. These data justify future larger studies to further assess dosing and efficacy of these intervention approaches. In conclusion, rehabilitation interventions that target spinal control of walking present a potential opportunity for enhancing walking function in older adults.

Neural markers of proactive and reactive cognitive control are altered during walking: A Mobile Brain-Body Imaging (MoBI) study

The processing of sensory information and the generation of motor commands needed to produce coordinated actions can interfere with ongoing cognitive tasks. Even simple motor behaviors like walking can alter cognitive task performance. This cognitive-motor interference (CMI) could arise from disruption of planning in anticipation of carrying out the task (proactive control) and/or from disruption of the execution of the task (reactive control). In young healthy adults, walking-induced interference with behavioral performance may not be readily observable because flexibility in neural circuits can compensate for the added demands of simultaneous loads. In this study, cognitive-motor loads were systematically increased during cued task-switching while underlying neurophysiologic changes in proactive and reactive mechanisms were measured. Brain activity was recorded from 22 healthy young adults using 64-channel electroencephalography (EEG) based Mobile Brain/Body Imaging (MoBI) as they alternately sat or walked during performance of cued task-switching. Walking altered neurophysiological indices of both proactive and reactive control. Walking amplified cue-evoked late fontal slow waves, and reduced the amplitude of target-evoked fronto-central N2 and parietal P3. The effects of walking on evoked neural responses systematically increased as the task became increasingly difficult. This may provide an objective brain marker of increasing cognitive load, and may prove to be useful in identifying seemingly healthy individuals who are currently able to disguise ongoing degenerative processes through active compensation. If, however, degeneration continues unabated these people may reach a compensatory limit at which point both cognitive performance and control of coordinated actions may decline rapidly.

Best-Compromise Control Strategy Between Mechanical Energy Expenditure and Foot Clearance for Obstacle-Crossing in Older Adults: Effects of Tai-Chi Chuan Practice

Background: Obstacle-crossing increases the risk of falls in older people. This study aimed to identify the effects of long-term Tai-Chi Chuan (TCC) practice on the control strategies for obstacle-crossing in older people.

Methods: A multi-objective optimal control technique with measured gait data was used to identify the control strategies adopted by 15 long-term TCC practitioners and 15 healthy controls when crossing obstacles of different heights, in terms of the best-compromise weighting sets for the conflicting objectives of minimizing energy expenditure and maximizing the toe-obstacle and heel-obstacle clearances.

Results and Conclusions: The long-term TCC older practitioners adopted a best-compromise control strategy similar to those adopted by young adults, with greater weightings on the minimization of the mechanical energy expenditure and smaller weightings on foot-clearance as compared to non-TCC controls (TCC: 0.72, 0.14, 0.14; Control: 0.55, 0.225, 0.225). This strategy enabled the long-term TCC older practitioners to cross obstacles with significantly greater leading-toe clearances but with relatively less mechanical energy expenditure. With the current approach, further simulations of obstacle-crossing mechanics with a given weighting set will be useful for answering clinically relevant what-if questions, such as what abilities would be needed if the non-TCC older people were to cross obstacles using the crossing strategy of the TCC people.

Differential Theta-Band Signatures of the Anterior Cingulate and Motor Cortices During Seated Locomotor Perturbations

Quantifying motor and cortical responses to perturbations during seated locomotor tasks such as recumbent stepping and cycling will expand and improve the understanding of locomotor adaptation processes beyond just perturbed gait. Using a perturbed recumbent stepping protocol, we hypothesized motor errors and anterior cingulate activity would decrease with time, and perturbation timing would influence electrocortical elicitation. Young adults (n = 17) completed four 10-minute arms and legs stepping tasks, with perturbations applied at every left or right leg extension-onset or mid-extension. A random no-perturbation "catch" stride occurred in every five perturbed strides. We instructed subjects to follow a pacing cue and to step smoothly, and we quantified temporal and spatial motor errors. We used high-density electroencephalography to estimate sources of electrocortical fluctuations shared among >70% of subjects. Temporal and spatial errors did not decrease from early to late for either perturbed or catch strides. Interestingly, spatial errors post-perturbation did not return to pre-perturbation levels, suggesting use-dependent learning occurred. Theta (3-8 Hz) synchronization in the anterior cingulate cortex and left and right supplementary motor areas (SMA) emerged near the perturbation event, and extension-onset perturbations elicited greater theta-band power than mid-extension perturbations. Even though motor errors did not adapt, anterior cingulate theta synchronization decreased from early to late perturbed strides, but only during the right-side tasks. Additionally, SMA mainly demonstrated specialized, not contralateral, lateralization. Overall, seated locomotor perturbations produced differential theta-band responses in the anterior cingulate and SMAs, suggesting that tuning perturbation parameters, e.g., timing, can potentially modify electrocortical responses.

Mobile EEG reveals functionally dissociable dynamic processes supporting real-world ambulatory obstacle avoidance: Evidence for early proactive control

The ability to safely negotiate the world on foot takes humans years to develop, reflecting the extensive cognitive demands associated with real-time planning and control of walking. Despite the importance of walking, methodological limitations mean that surprisingly little is known about the neural and cognitive processes that support ambulatory motor control. Here, we report mobile EEG data recorded from 32 healthy young adults during real-world ambulatory obstacle avoidance. Participants walked along a path while stepping over expected and unexpected obstacles projected on the floor, allowing us to capture the dynamic oscillatory response to changes in environmental demands. Compared to obstacle-free walking, time-frequency analysis of the EEG data revealed clear neural markers of proactive and reactive forms of movement control (occurring before and after crossing an obstacle), visible as increases in frontal theta and centro-parietal beta power respectively. Critically, the temporal profile of changes in frontal theta allowed us to arbitrate between early selection and late adaptation mechanisms of proactive control. Our data show that motor plans are updated as soon as an upcoming obstacle appears, rather than when the obstacle is reached. In addition, regardless of whether motor plans required updating, a clear beta rebound was present after obstacles were crossed, reflecting the resetting of the motor system. Overall, mobile EEG recorded during real-world walking provides novel insight into the cognitive and neural basis of dynamic motor control in humans, suggesting new routes to the monitoring and rehabilitation of motor disorders such as dyspraxia and Parkinson's disease.

Obstacle Negotiation, Gait Variability, and Risk of Falling: Results From the "Gait and Brain Study"

BACKGROUND

Gait variability is an early fall predictor. However, it is unknown how gait variability of older adults at high risk of falls is affected by an obstacle negotiation task. We aimed to compare gait performance between older adults with significant history of falls (i.e. fallers) and nonfallers while approaching an obstacle crossing.

METHODS

A total of 137 older adults without dementia were enrolled (72.7 ± 5.1 years of age; 60.5% women) from the "Gait and Brain Study." Fallers were defined as having at least one injurious fall or at least two noninjurious falls in the previous 12 months. Participants performed gait assessments under unobstructed and obstructed conditions. During the obstructed condition, participants walked and stepped over an ad hoc obstacle set at 15% of participants' height, transversally placed on a 6-meter electronic walkway. Gait speed and step-to-step variabilities were quantified from the last six steps prior to obstacle crossing. Analysis of variance models adjusted for age, sex, fear of falling, comorbidities, and unobstructed gait were used to compare gait performance of fallers and nonfallers during an obstacle approaching.

RESULTS

In the study, 27 older adults were identified as fallers and 110 as nonfallers. Fallers had higher step time variability and step length variability when approaching an obstacle compared with nonfallers, although groups had comparable gait performance during unobstructed walking.

CONCLUSION

Gait variability of older individuals at high risk of falling is more disturbed, compared with low-risk individuals, while approaching an obstacle crossing. High gait variability prior to crossing an obstacle may be a risk factor for falls.

Electrocorticographic Encoding of Human Gait in the Leg Primary Motor Cortex

While prior noninvasive (e.g., electroencephalographic) studies suggest that the human primary motor cortex (M1) is active during gait processes, the limitations of noninvasive recordings make it impossible to determine whether M1 is involved in high-level motor control (e.g., obstacle avoidance, walking speed), low-level motor control (e.g., coordinated muscle activation), or only nonmotor processes (e.g., integrating/relaying sensory information). This study represents the first invasive electroneurophysiological characterization of the human leg M1 during walking. Two subjects with an electrocorticographic grid over the interhemispheric M1 area were recruited. Both exhibited generalized γ-band (40-200 Hz) synchronization across M1 during treadmill walking, as well as periodic γ-band changes within each stride (across multiple walking speeds). Additionally, these changes appeared to be of motor, rather than sensory, origin. However, M1 activity during walking shared few features with M1 activity during individual leg muscle movements, and was not highly correlated with lower limb trajectories on a single channel basis. These findings suggest that M1 primarily encodes high-level gait motor control (i.e., walking duration and speed) instead of the low-level patterns of leg muscle activation or movement trajectories. Therefore, M1 likely interacts with subcortical/spinal networks, which are responsible for low-level motor control, to produce normal human walking.

Trial-by-trial source-resolved EEG responses to gait task challenges predict subsequent step adaptation

A growing body of evidence indicates a pivotal role of cognition and in particular executive function in gait control and fall prevention. In a recent gait study using electroencephalographic (EEG) imaging, we provided direct proof for cortical top-down inhibitory control in step adaptation. A crucial part of motor inhibition is recognizing stimuli that signal the need to inhibit or adjust motor actions such as steps during walking. One of the EEG signatures of performance monitoring in response to events signaling the need to adjust motor responses, are error-related potential (error-ERP) features. To examine whether error-ERP features may index executive control during gait adaptation, we analyzed high-density (108-channel) EEG data from an auditory gait pacing study. Participants (N = 18) walking on a steadily moving treadmill were asked to step in time to an auditory cue tone sequence, and then to quickly adapt their step length and rate, to regain step-cue synchrony following occasional unexpected shifts in the pacing cue train to a faster or slower cue tempo. Decomposition of the continuous EEG data by independent component analysis revealed a negative deflection in the source-resolved event-related potential (ERP) time locked to 'late' cue tones marking a shift to a slower cue tempo. This vertex-negative ERP feature, localized primarily to posterior medial frontal cortex (pMFC) and peaking 250 ms after the onset of the tempo-shift cue, we here refer to as the step-cue delay negativity (SDN). SDN source, timing, and polarity resemble other error-related ERP features, e.g., the Error-Related Negativity (ERN) and Feedback-Related Negativity (FRN) in (seated) button press response tasks. In single trials, SDN amplitude varied with the magnitude of the cue latency deviation (the time interval between the expected and actual cue onsets). Regression analysis also identified linear coupling between SDN amplitude and the subsequent speed of gait tempo adaptation (as measured by the increase in length of the ensuing adaptation step). The SDN in this paradigm thus seems both to index the perceived need for and the subsequent magnitude of the immediate gait adjustment, consistent with performance-monitoring models. Future research might investigate relationships of these control processes to the impairment of gait adjustment in motor disorders and cognitive decline, for example to develop a biomarker for fall risk prediction in early-stage Parkinson's.

Human electrocortical dynamics while stepping over obstacles

To better understand human brain dynamics during visually guided locomotion, we developed a method of removing motion artifacts from mobile electroencephalography (EEG) and studied human subjects walking and running over obstacles on a treadmill. We constructed a novel dual-layer EEG electrode system to isolate electrocortical signals, and then validated the system using an electrical head phantom and robotic motion platform. We collected data from young healthy subjects walking and running on a treadmill while they encountered unexpected obstacles to step over. Supplementary motor area and premotor cortex had spectral power increases within ~200 ms after object appearance in delta, theta, and alpha frequency bands (3–13 Hz). That activity was followed by similar posterior parietal cortex spectral power increase that decreased in lag time with increasing locomotion speed. The sequence of activation suggests that supplementary motor area and premotor cortex interrupted the gait cycle, while posterior parietal cortex tracked obstacle location for planning foot placement nearly two steps ahead of reaching the obstacle. Together, these results highlight advantages of adopting dual-layer mobile EEG, which should greatly facilitate the study of human brain dynamics in physically active real-world settings and tasks.

Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion

The appreciation for the need to record electroencephalographic (EEG) signals from humans while walking has been steadily growing in recent years, particularly in relation to understanding gait disturbances. Movement artefacts (MA) in EEG signals originate from mechanical forces applied to the scalp electrodes, inducing small electrode movements relative to the scalp which, in turn, cause the recorded voltage to change irrespectively of cortical activity. These mechanical forces, and thus MA, may have various sources (e.g., ground reaction forces, head movements, etc.) that are inherent to daily activities, notably walking. In this paper we introduce a systematic, integrated methodology for removing MA from EEG signals recorded during treadmill (TM) and over-ground (OG) walking, as well as quantify the prevalence of MA in different locomotion settings. In our experiments, participants performed walking trials at various speeds both OG and on a TM while wearing a 32-channel EEG cap and a 3-axis accelerometer, placed on the forehead. Data preprocessing included separating the EEG signals into statistically independent additive components using independent component analysis (ICA). We observed an increase in electro-physiological signals (e.g., neck EMG activations for stabilizing the head during heel-strikes) as the walking speed increased. These artefact independent-components (ICs), while not originating from electrode movement, still exhibit a similar spectral pattern to the MA ICs–a peak at the stepping frequency. MA was identified and quantified in each component using a novel method that utilizes the participant’s stepping frequency, derived from a forehead-mounted accelerometer. We then benchmarked the EEG data by applying newly established metrics to quantify the success of our method in cleaning the data. The results indicate that our approach can be successfully applied to EEG data recorded during TM and OG walking, and is offered as a unified methodology for MA removal from EEG collected during gait trials.

Cooperative hand movements: task-dependent modulation of ipsi- and contralateral cortical control

Cooperative hand movements are known to be controlled by a task-specific neural coupling associated with an involvement of the respective ipsilateral hemispheres. The aim of this study was to explore in how far this neural control applies to and is modulated during various, fine and gross, cooperative hand movements required during activities of daily living. Somatosensory evoked potentials and contralateral electromyographic reflex responses to unilateral ulnar nerve stimulation were simultaneously recorded in healthy participants during three different cooperative hand movement tasks and a resting condition. Amplitude ratio (ipsi-/contralateral) of the somatosensory evoked potentials, which is a measure for the involvement of the ipsilateral hemisphere in movement control, was higher in all three movement tasks compared to resting. This ratio was highest during the fine cooperative movement studied here. Contralateral reflex responses, as a measure for the functional coupling of the arms, were elicited following stimulation of both arms during gross cooperative movements. However, such a response could only be elicited in the dominant arm during fine movement. It is concluded that the neural coupling and thus enhancement of ipsilateral cortical control is preserved through different cooperative hand movement tasks, independently whether fine or gross motor tasks are performed. However, modulation of cortical control can be observed as ipsilateral cortical control is stronger during fine movements and functional coupling of the arms more focused to the dominant hand compared to gross cooperative tasks.

Assessing Brain-Muscle Connectivity in Human Locomotion through Mobile Brain/Body Imaging: Opportunities, Pitfalls, and Future Directions

Assessment of the cortical role during bipedalism has been a methodological challenge. While surface electroencephalography (EEG) is capable of non-invasively measuring cortical activity during human locomotion, it is associated with movement artifacts obscuring cerebral sources of activity. Recently, statistical methods based on blind source separation revealed potential for resolving this issue, by segregating non-cerebral/artifactual from cerebral sources of activity. This step marked a new opportunity for the investigation of the brains' role while moving and was tagged mobile brain/body imaging (MoBI). This methodology involves simultaneous mobile recording of brain activity with several other body behavioral variables (e.g., muscle activity and kinematics), through wireless recording wearable devices/sensors. Notably, several MoBI studies using EEG-EMG approaches recently showed that the brain is functionally connected to the muscles and active throughout the whole gait cycle and, thus, rejecting the long-lasting idea of a solely spinal-driven bipedalism. However, MoBI and brain/muscle connectivity assessments during human locomotion are still in their fledgling state of investigation. Mobile brain/body imaging approaches hint toward promising opportunities; however, there are some remaining pitfalls that need to be resolved before considering their routine clinical use. This article discusses several of these pitfalls and proposes research to address them. Examples relate to the validity, reliability, and reproducibility of this method in ecologically valid scenarios and in different populations. Furthermore, whether brain/muscle connectivity within the MoBI framework represents a potential biomarker in neuromuscular syndromes where gait disturbances are evident (e.g., age-related sarcopenia) remains to be determined.

Walking behavior over multiple obstacles in people with Parkinson's disease

The presence of a second obstacle changed the planning and adjustments for obstacle avoidance performance, but this context is poorly understood in Parkinson's disease (PD). The aim of this study was to investigate the walking behavior over multiple obstacles in people with PD. Nineteen people with PD and 19 healthy individuals walked across an 8m pathway, performing three trials for following conditions: unobstructed walking, walking with one obstacle avoidance (Single), and walking with two obstacles avoidance (Double). In the Double condition, the analysis was performed only for the first obstacle (First Double). The dependent variables were calculated separately for the approach and crossing phases in the obstacle conditions. The main results show that people with PD decreased single support and increased double support phase in both Single and Double conditions compared to the unobstructed walking. Both groups increased stride duration during approach phase in the Double condition compared to the unobstructed walking and Single conditions. The presence of the second obstacle led to a decrease in trailing toe clearance during obstacle avoidance of the First Double. In conclusion, people with PD use a conservative strategy while approaching obstacles. Both groups need more time to obtain and process environmental information and plan the action in environments with multiple obstacles. The smaller leading toe clearance might be an indicative that the presence of a second obstacle increase the likelihood of tripping during obstacle avoidance in both people with PD and healthy individuals.

The role of virtual reality in improving motor performance as revealed by EEG: a randomized clinical trial

BACKGROUND

Many studies have demonstrated the usefulness of repetitive task practice by using robotic-assisted gait training (RAGT) devices, including Lokomat, for the treatment of lower limb paresis. Virtual reality (VR) has proved to be a valuable tool to improve neurorehabilitation training. The aim of our pilot randomized clinical trial was to understand the neurophysiological basis of motor function recovery induced by the association between RAGT (by using Lokomat device) and VR (an animated avatar in a 2D VR) by studying electroencephalographic (EEG) oscillations.

METHODS

Twenty-four patients suffering from a first unilateral ischemic stroke in the chronic phase were randomized into two groups. One group performed 40 sessions of Lokomat with VR (RAGT + VR), whereas the other group underwent Lokomat without VR (RAGT-VR). The outcomes (clinical, kinematic, and EEG) were measured before and after the robotic intervention.

RESULTS

As compared to the RAGT-VR group, all the patients of the RAGT + VR group improved in the Rivermead Mobility Index and Tinetti Performance Oriented Mobility Assessment. Moreover, they showed stronger event-related spectral perturbations in the high-γ and β bands and larger fronto-central cortical activations in the affected hemisphere.

CONCLUSIONS

The robotic-based rehabilitation combined with VR in patients with chronic hemiparesis induced an improvement in gait and balance. EEG data suggest that the use of VR may entrain several brain areas (probably encompassing the mirror neuron system) involved in motor planning and learning, thus leading to an enhanced motor performance.

TRIAL REGISTRATION

Retrospectively registered in Clinical Trials on 21-11-2016, n. NCT02971371 .

Neuroimaging of Human Balance Control: A Systematic Review

This review examined 83 articles using neuroimaging modalities to investigate the neural correlates underlying static and dynamic human balance control, with aims to support future mobile neuroimaging research in the balance control domain. Furthermore, this review analyzed the mobility of the neuroimaging hardware and research paradigms as well as the analytical methodology to identify and remove movement artifact in the acquired brain signal. We found that the majority of static balance control tasks utilized mechanical perturbations to invoke feet-in-place responses (27 out of 38 studies), while cognitive dual-task conditions were commonly used to challenge balance in dynamic balance control tasks (20 out of 32 studies). While frequency analysis and event related potential characteristics supported enhanced brain activation during static balance control, that in dynamic balance control studies was supported by spatial and frequency analysis. Twenty-three of the 50 studies utilizing EEG utilized independent component analysis to remove movement artifacts from the acquired brain signals. Lastly, only eight studies used truly mobile neuroimaging hardware systems. This review provides evidence to support an increase in brain activation in balance control tasks, regardless of mechanical, cognitive, or sensory challenges. Furthermore, the current body of literature demonstrates the use of advanced signal processing methodologies to analyze brain activity during movement. However, the static nature of neuroimaging hardware and conventional balance control paradigms prevent full mobility and limit our knowledge of neural mechanisms underlying balance control.

Modular organization of muscle activity patterns in the leading and trailing limbs during obstacle clearance in healthy adults

Human locomotor patterns require precise adjustments to successfully navigate complex environments. Studies suggest that the central nervous system may control such adjustments through supraspinal signals modifying a basic locomotor pattern at the spinal level. To explore this proposed control mechanism in the leading and trailing limbs during obstructed walking, healthy young adults stepped over obstacles measuring 0.1 and 0.2 m in height. Unobstructed walking with no obstacle present was also performed as a baseline. Full body three-dimensional kinematic data were recorded and electromyography (EMG) was collected from 14 lower limb muscles on each side of the body. EMG data were analyzed using two techniques: by mapping the EMG data to the approximate location of the motor neuron pools on the lumbosacral enlargement of the spinal cord and by applying a nonnegative matrix factorization algorithm to unilateral and bilateral muscle activations separately. Results showed that obstacle clearance may be achieved not only with the addition of a new activation pattern in the leading limb, but with a temporal shift of a pattern present during unobstructed walking in both the leading and trailing limbs. An investigation of the inter-limb coordination of these patterns suggested a strong bilateral linkage between lower limbs. These results highlight the modular organization of muscle activation in the leading and trailing limbs, as well as provide a mechanism of control when implementing a locomotor adjustment when stepping over an obstacle.

Alterations of Muscle Activation Pattern in Stroke Survivors during Obstacle Crossing

OBJECTIVE

This study investigates changes in the neuromuscular activation pattern of the lower limb muscles in stroke survivors when crossing obstacles of three different heights.

METHODS

Eight stroke survivors and eight age-, height-, and gender-matched healthy controls were recruited and instructed to cross over obstacles with heights of 10, 20, and 30% leg length. Surface electromyography (EMG) signals were recorded from the rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and medial gastrocnemius (MG) of both limbs. Muscle activation signals were normalized to maximum voluntary contraction. Differences between groups and heights were compared using the root mean square of EMG, the cocontraction index of agonist and antagonist muscles, and power spectral analysis based on the mean power frequency (MPF). The correlations between the calculated variables and clinical scales such as Berg Balance Scale and Fugl-Meyer assessment (FMA) were also examined.

RESULTS

During the leading limb swing phase, the activation levels of all four muscles were greater in the stroke group than the healthy controls (p < 0.05), and the TA showed increased activation level with increasing obstacle height in both groups (p < 0.05). Cocontraction between the TA and MG was higher in the stroke group during the swing phase of the leading limb and between the RF and BF during the stance phase (p < 0.05). Similarly, for the trailing limb, increased cocontractions between the two pairs of agonist and antagonist muscles were found during the stance phase in the stroke group (p < 0.05). During the crossing stride, the frequency analysis showed significantly smaller MPF values in all four lower limb muscles in the leading limb of stroke survivors compared with healthy controls (p < 0.05). Moreover, significant correlations were found between the FMA scores and the BF and TA activations in the leading limb during the swing phase (p < 0.05).

CONCLUSION

Greater activation levels of the lower limb muscles resulted in higher muscular demands for stroke survivors, which might lead to greater difficulty in maintaining balance. The increased cocontraction during obstacle crossing might be compensation for the affected stability and enable safe crossing for stroke survivors. The reduced MPF in the affected limb of the stroke group might be due to impairments in motor units or other complex neuromuscular alterations.

Distinct β Band Oscillatory Networks Subserving Motor and Cognitive Control during Gait Adaptation

Everyday locomotion and obstacle avoidance requires effective gait adaptation in response to sensory cues. Many studies have shown that efficient motor actions are associated with μ rhythm (8-13 Hz) and β band (13-35 Hz) local field desynchronizations in sensorimotor and parietal cortex, whereas a number of cognitive task studies have reported higher behavioral accuracy to be associated with increases in β band power in prefrontal and sensory cortex. How these two distinct patterns of β band oscillations interplay during gait adaptation, however, has not been established. Here we recorded 108 channel EEG activity from 18 participants (10 males, 22-35 years old) attempting to walk on a treadmill in synchrony with a series of pacing cue tones, and quickly adapting their step rate and length to sudden shifts in pacing cue tempo. Independent component analysis parsed each participant's EEG data into maximally independent component (IC) source processes, which were then grouped across participants into distinct spatial/spectral clusters. Following cue tempo shifts, mean β band power was suppressed for IC sources in central midline and parietal regions, whereas mean β band power increased in IC sources in or near medial prefrontal and dorsolateral prefrontal cortex. In the right dorsolateral prefrontal cortex IC cluster, the β band power increase was stronger during (more effortful) step shortening than during step lengthening. These results thus show that two distinct patterns of β band activity modulation accompany gait adaptations: one likely serving movement initiation and execution; and the other, motor control and inhibition.

SIGNIFICANCE STATEMENT

Understanding brain dynamics supporting gait adaptation is crucial for understanding motor deficits in walking, such as those associated with aging, stroke, and Parkinson's. Only a few electromagnetic brain imaging studies have examined neural correlates of human upright walking. Here, application of independent component analysis to EEG data recorded during treadmill walking allowed us to uncover two distinct β band oscillatory cortical networks that are active during gait adaptation to shifts in the tempo of an auditory pacing cue: (8-13 Hz) μ rhythm and (13-35 Hz) β band power decreases in central and parietal cortex and (14-20 Hz) β band power increases in frontal brain areas. These results provide a fuller framework for electrophysiological studies of cortical gait control and its disorders.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

Electrocortical activity distinguishes between uphill and level walking in humans

The objective of this study was to determine if electrocortical activity is different between walking on an incline compared with level surface. Subjects walked on a treadmill at 0% and 15% grades for 30 min while we recorded electroencephalography (EEG). We used independent component (IC) analysis to parse EEG signals into maximally independent sources and then computed dipole estimations for each IC. We clustered cortical source ICs and analyzed event-related spectral perturbations synchronized to gait events. Theta power fluctuated across the gait cycle for both conditions, but was greater during incline walking in the anterior cingulate, sensorimotor and posterior parietal clusters. We found greater gamma power during level walking in the left sensorimotor and anterior cingulate clusters. We also found distinct alpha and beta fluctuations, depending on the phase of the gait cycle for the left and right sensorimotor cortices, indicating cortical lateralization for both walking conditions. We validated the results by isolating movement artifact. We found that the frequency activation patterns of the artifact were different than the actual EEG data, providing evidence that the differences between walking conditions were cortically driven rather than a residual artifact of the experiment. These findings suggest that the locomotor pattern adjustments necessary to walk on an incline compared with level surface may require supraspinal input, especially from the left sensorimotor cortex, anterior cingulate, and posterior parietal areas. These results are a promising step toward the use of EEG as a feed-forward control signal for ambulatory brain-computer interface technologies.

Analyzing EEG signals to detect unexpected obstacles during walking

Background

When an unexpected perturbation in the environment occurs, the subsequent alertness state may cause a brain activation responding to that perturbation which can be detected and employed by a Brain-Computer Interface (BCI). In this work, the possibility of detecting a sudden obstacle appearance analyzing electroencephalographic (EEG) signals is assessed. For this purpose, different features of EEG signals are evaluated during the appearance of sudden obstacles while a subject is walking on a treadmill. The future goal is to use this procedure to detect any obstacle appearance during walking when the user is wearing a lower limb exoskeleton in order to generate an emergency stop command for the exoskeleton. This would enhance the user-exoskeleton interaction, improving the safety mechanisms of current exoskeletons.

Methods

In order to detect the change in the brain activity when an obstacle suddenly appears, different features of EEG signals are evaluated using the recordings of five healthy subjects. Since the change in the brain activity occurs in the time domain, the features evaluated are: common spatial patterns, average power, slope, and the coefficients of a polynomial fit. A Linear Discriminant Analysis-based classifier is used to differentiate between two conditions: the appearance or not of an obstacle. The evaluation of the performance to detect the obstacles is made in terms of accuracy, true positive (TP) and false positive (FP) rates.

Results

From the offline analysis, the best performance is achieved when the slope or the polynomial coefficients are used as features, with average detection accuracy rates of 74.0 and 79.5 %, respectively. These results are consistent with the pseudo-online results, where a complete EEG recording is segmented into windows of 500 ms and overlapped 400 ms, and a decision about the obstacle appearance is made for each window. The results of the best subject were 11 out of 14 obstacles detected with a rate of 9.09 FPs/min, and 10 out of 14 obstacles detected with a rate of 6.34 FPs/min using slope and polynomial coefficients features, respectively.

Conclusions

An EEG-based BCI can be developed to detect the appearance of unexpected obstacles. The average accuracy achieved is 79.5 % of success rate with a low number of false detections. Thus, the online performance of the BCI would be suitable for commanding in a safely way a lower limb exoskeleton during walking.

EEG Single-Trial Detection of Gait Speed Changes during Treadmill Walk

In this study, we analyse the electroencephalography (EEG) signal associated with gait speed changes (i.e. acceleration or deceleration). For data acquisition, healthy subjects were asked to perform volitional speed changes between 0, 1, and 2 Km/h, during treadmill walk. Simultaneously, the treadmill controller modified the speed of the belt according to the subject’s linear speed. A classifier is trained to distinguish between the EEG signal associated with constant speed gait and with gait speed changes, respectively. Results indicate that the classification performance is fair to good for the majority of the subjects, with accuracies always above chance level, in both batch and pseudo-online approaches. Feature visualisation and equivalent dipole localisation suggest that the information used by the classifier is associated with increased activity in parietal areas, where mu and beta rhythms are suppressed during gait speed changes. Specifically, the parietal cortex may be involved in motor planning and visuomotor transformations throughout the online gait adaptation, which is in agreement with previous research. The findings of this study may help to shed light on the cortical involvement in human gait control, and represent a step towards a BMI for applications in post-stroke gait rehabilitation.

It's how you get there: walking down a virtual alley activates premotor and parietal areas

Voluntary drive is crucial for motor learning, therefore we are interested in the role that motor planning plays in gait movements. In this study we examined the impact of an interactive Virtual Environment (VE) feedback task on the EEG patterns during robot assisted walking. We compared walking in the VE modality to two control conditions: walking with a visual attention paradigm, in which visual stimuli were unrelated to the motor task; and walking with mirror feedback, in which participants observed their own movements. Eleven healthy participants were considered. Application of independent component analysis to the EEG revealed three independent component clusters in premotor and parietal areas showing increased activity during walking with the adaptive VE training paradigm compared to the control conditions. During the interactive VE walking task spectral power in frequency ranges 8-12, 15-20, and 23-40 Hz was significantly (p ≤ 0.05) decreased. This power decrease is interpreted as a correlate of an active cortical area. Furthermore activity in the premotor cortex revealed gait cycle related modulations significantly different (p ≤ 0.05) from baseline in the frequency range 23-40 Hz during walking. These modulations were significantly (p ≤ 0.05) reduced depending on gait cycle phases in the interactive VE walking task compared to the control conditions. We demonstrate that premotor and parietal areas show increased activity during walking with the adaptive VE training paradigm, when compared to walking with mirror- and movement unrelated feedback. Previous research has related a premotor-parietal network to motor planning and motor intention. We argue that movement related interactive feedback enhances motor planning and motor intention. We hypothesize that this might improve gait recovery during rehabilitation.

Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator

The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

Effects of task constraints on obstacle avoidance strategies in patients with cerebellar disease

The present study examined the effects of cerebellar disease on the organization and execution of obstacle avoidance tasks. To this end, we characterized how variations in the execution demands of the subsequent obstacles in multiple obstacle crossing tasks influenced the stepping performance of the initial obstacle in patients with cerebellar degeneration (CD) by manipulating the height (6 cm and 16 cm) and distance (1 m and 2 m) of the second obstacle. Nine patients with bilateral cerebellar atrophy and nine age-matched normal controls were instructed to walk along an 8 m long pathway and step over two obstacles without contacting them. The primary finding indicated that CD patients exhibited an elevated foot clearance over the initial obstacle when the height demand of the second obstacle was increased. Such abnormal step-height adjustments in CD patients are considered as an adaptive avoidance strategy to diminish the execution demands of complex obstacle tasks and to enhance safe performance. These results suggest that the cerebellum is important for the implementation of optimal stepping strategies to be used during multiple obstacle crossings in which the obstacles have different execution demands.

Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects

In robot assisted gait training, a pattern of human locomotion is executed repetitively with the intention to restore the motor programs associated with walking. Several studies showed that active contribution to the movement is critical for the encoding of motor memory. We propose to use brain monitoring techniques during gait training to encourage active participation in the movement. We investigated the spectral patterns in the electroencephalogram (EEG) that are related to active and passive robot assisted gait. Fourteen healthy participants were considered. Infomax independent component analysis separated the EEG into independent components representing brain, muscle, and eye movement activity, as well as other artifacts. An equivalent current dipole was calculated for each independent component. Independent components were clustered across participants based on their anatomical position and frequency spectra. Four clusters were identified in the sensorimotor cortices that accounted for differences between active and passive walking or showed activity related to the gait cycle. We show that in central midline areas the mu (8-12 Hz) and beta (18-21 Hz) rhythms are suppressed during active compared to passive walking. These changes are statistically significant: mu (F(1, 13)=11.2 p ≤ 0.01) and beta (F(1, 13)=7.7, p ≤ 0.05). We also show that these differences depend on the gait cycle phases. We provide first evidence of modulations of the gamma rhythm in the band 25 to 40 Hz, localized in central midline areas related to the phases of the gait cycle. We observed a trend (F(1, 8)=11.03, p ≤ 0.06) for suppressed low gamma rhythm when comparing active and passive walking. Additionally we found significant suppressions of the mu (F(1, 11)=20.1 p ≤ 0.01), beta (F(1, 11)=11.3 p ≤ 0.05) and gamma (F(1, 11)=4.9 p ≤ 0.05) rhythms near C3 (in the right hand area of the primary motor cortex) during phases of active vs. passive robot assisted walking. To our knowledge this is the first study showing EEG analysis during robot assisted walking. We provide evidence for significant differences in cortical activation between active and passive robot assisted gait. Our findings may help to define appropriate features for single trial detection of active participation in gait training. This work is a further step toward the evaluation of brain monitoring techniques and brain-computer interface technologies for improving gait rehabilitation therapies in a top-down approach.

From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation

Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.