Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking

Mobile brain/body imaging of landmark-based navigation with high-density EEG

Coupling behavioral measures and brain imaging in naturalistic, ecological conditions is key to comprehend the neural bases of spatial navigation. This highly integrative function encompasses sensorimotor, cognitive, and executive processes that jointly mediate active exploration and spatial learning. However, most neuroimaging approaches in humans are based on static, motion‐constrained paradigms and they do not account for all these processes, in particular multisensory integration. Following the Mobile Brain/Body Imaging approach, we aimed to explore the cortical correlates of landmark‐based navigation in actively behaving young adults, solving a Y‐maze task in immersive virtual reality. EEG analysis identified a set of brain areas matching state‐of‐the‐art brain imaging literature of landmark‐based navigation. Spatial behavior in mobile conditions additionally involved sensorimotor areas related to motor execution and proprioception usually overlooked in static fMRI paradigms. Expectedly, we located a cortical source in or near the posterior cingulate, in line with the engagement of the retrosplenial complex in spatial reorientation. Consistent with its role in visuo‐spatial processing and coding, we observed an alpha‐power desynchronization while participants gathered visual information. We also hypothesized behavior‐dependent modulations of the cortical signal during navigation. Despite finding few differences between the encoding and retrieval phases of the task, we identified transient time–frequency patterns attributed, for instance, to attentional demand, as reflected in the alpha/gamma range, or memory workload in the delta/theta range. We confirmed that combining mobile high‐density EEG and biometric measures can help unravel the brain structures and the neural modulations subtending ecological landmark‐based navigation.

Noise-assisted Multivariate Empirical Mode Decomposition based Causal Decomposition for brain-physiological network in bivariate and multiscale time series

Objective.Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) based Causal Decomposition depicts a cause and effect relationship that is not based on the term of prediction, but rather on the phase dependence of time series. Here, we present the NA-MEMD based Causal Decomposition approach according to the covariation and power views traced to Hume and Kant: a priori cause-effect interaction is first acquired, and the presence of a candidate cause and of the effect is then computed from the sensory input somehow.Approach.Based on the definition of NA-MEMD based Causal Decomposition, we show such causal relation is a phase relation where the candidate causes are not merely followed by effects, but rather produce effects.Main results.The predominant methods used in neuroscience (Granger causality, EMD-based Causal Decomposition) are validated, showing the applicability of NA-MEMD based Causal Decomposition, particular to brain physiological processes in bivariate and multiscale time series.Significance.We point to the potential use in the causality inference analysis in a complex dynamic process.

Overground Walking Decreases Alpha Activity and Entrains Eye Movements in Humans

Experiments in animal models have shown that running increases neuronal activity in early visual areas in light as well as in darkness. This suggests that visual processing is influenced by locomotion independent of visual input. Combining mobile electroencephalography, motion- and eye-tracking, we investigated the influence of overground free walking on cortical alpha activity (~10 Hz) and eye movements in healthy humans. Alpha activity has been considered a valuable marker of inhibition of sensory processing and shown to negatively correlate with neuronal firing rates. We found that walking led to a decrease in alpha activity over occipital cortex compared to standing. This decrease was present during walking in darkness as well as during light. Importantly, eye movements could not explain the change in alpha activity. Nevertheless, we found that walking and eye related movements were linked. While the blink rate increased with increasing walking speed independent of light or darkness, saccade rate was only significantly linked to walking speed in the light. Pupil size, on the other hand, was larger during darkness than during light, but only showed a modulation by walking in darkness. Analyzing the effect of walking with respect to the stride cycle, we further found that blinks and saccades preferentially occurred during the double support phase of walking. Alpha power, as shown previously, was lower during the swing phase than during the double support phase. We however could exclude the possibility that the alpha modulation was introduced by a walking movement induced change in electrode impedance. Overall, our work indicates that the human visual system is influenced by the current locomotion state of the body. This influence affects eye movement pattern as well as neuronal activity in sensory areas and might form part of an implicit strategy to optimally extract sensory information during locomotion.

A pilot study assessing reliability and age-related differences in corticomuscular and intramuscular coherence in ankle dorsiflexors during walking

Corticomuscular (CMC) and intramuscular (intraMC) coherence represent measures of corticospinal interaction. Both CMC and intraMC can be assessed during human locomotion tasks, for example, while walking. Corticospinal control of gait can deteriorate during the aging process and CMC and intraMC may represent an important monitoring means. However, it is unclear whether such assessments represent a reliable tool when performed during walking in an ecologically valid scenario and whether age‐related differences may occur. Wireless surface electroencephalography and electromyography were employed in a pilot study with young and old adults during overground walking in two separate sessions. CMC and intraMC analyses were performed in the gathered beta and lower gamma frequencies (i.e., 13–40 Hz). Significant log‐transformed coherence area was tested for intersessions test–retest reliability by determining intraclass correlation coefficient (ICC), yielding to low reliability in CMC in both younger and older adults. intraMC exclusively showed low reliability in the older adults, whereas intraMC in the younger adults revealed similar values as previously reported: test–retest reliability [ICC (95% CI): 0.44 (−0.23, 0.87); SEM: 0.46; MDC: 1.28; MDC%: 103; Hedge's g (95% CI): 0.54 (−0.13, 1.57)]. Significant differences between the age groups were observed in intraMC by either comparing the two groups with the first test [Hedge's g (95% CI): 1.55 (0.85, 2.15); p‐value: .006] or with the retest data [Hedge's g (95% CI): 2.24 (0.73, 3.70); p‐value: .005]. Notwithstanding the small sample size investigated, intraMC seems a moderately reliable assessment in younger adults. The further development and use of this measure in practical settings to infer corticospinal interaction in human locomotion in clinical practice is warranted and should help to refine the analysis. This necessitates involving larger sample sizes as well as including a wider number of lower limb muscles. Moreover, further research seems warranted by the observed differences in modulation mechanisms of corticospinal control of gait as ascertained by intraMC between the age groups.

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

Despite the advances in the field of brain computer interfaces (BCI), the use of the sole electroencephalography (EEG) signal to control walking rehabilitation devices is currently not viable in clinical settings, due to its unreliability. Hybrid interfaces (hHMIs) represent a very recent solution to enhance the performance of single-signal approaches. These are classification approaches that combine multiple human-machine interfaces, normally including at least one BCI with other biosignals, such as the electromyography (EMG). However, their use for the decoding of gait activity is still limited. In this work, we propose and evaluate a hybrid human-machine interface (hHMI) to decode walking phases of both legs from the Bayesian fusion of EEG and EMG signals. The proposed hHMI significantly outperforms its single-signal counterparts, by providing high and stable performance even when the reliability of the muscular activity is compromised temporarily (e.g., fatigue) or permanently (e.g., weakness). Indeed, the hybrid approach shows a smooth degradation of classification performance after temporary EMG alteration, with more than 75% of accuracy at 30% of EMG amplitude, with respect to the EMG classifier whose performance decreases below 60% of accuracy. Moreover, the fusion of EEG and EMG information helps keeping a stable recognition rate of each gait phase of more than 80% independently on the permanent level of EMG degradation. From our study and findings from the literature, we suggest that the use of hybrid interfaces may be the key to enhance the usability of technologies restoring or assisting the locomotion on a wider population of patients in clinical applications and outside the laboratory environment.

Time-Frequency Maximal Information Coefficient Method and its Application to Functional Corticomuscular Coupling

An important challenge in the study of functional corticomuscular coupling (FCMC) is an accurate capture of the coupling relationship between the cerebral cortex and the effector muscle. The coherence method is a linear analysis method, which has certain limitations in further revealing the nonlinear coupling between neural signals. Although mutual information (MI) and transfer entropy (TE) based on information theory can capture both linear and nonlinear correlations, the equitability of these algorithms is ignored and the nonlinear components of the correlation cannot be separated. The maximal information coefficient (MIC) is a suitable method to measure the coupling between neurophysiological signals. This study extends the MIC to the time-frequency domain, named time-frequency maximal information coefficient (TFMIC), to explore the FCMC in a specific frequency band. The effectiveness, equitability, and robustness of the algorithm on the simulation data was verified and compared with coherence, TE- and MI- based methods. Simulation results showed that the TFMIC could accurately detect the coupling for different functional relationships at low noise levels. The dorsiflexion experimental results revealed that the beta-band (14-30 Hz) significant coupling was observed at channels Cz, C4, FC4, and FCz. Additionally, the results showed that the coupling was higher in the alpha-band (8-13 Hz) and beta-band (14-30 Hz) than in the gamma-band (31-45 Hz). This might be related to a transition between sensorimotor states. Specifically, the nonlinear component of FCMC was also observed at channels Cz, C4, FC4, and FCz. This study expanded the research on nonlinear coupling components in FCMC.

Phase dependent modulation of cortical activity during action observation and motor imagery of walking: An EEG study

Action observation (AO) and motor imagery (MI) are motor simulations which induce cortical activity related to execution of observed and imagined movements. Neuroimaging studies have mainly investigated where the cortical activities during AO and MI of movements are activated and if they match those activated during execution of the movements. However, it remains unclear how cortical activity is modulated; in particular, whether activity depends on observed or imagined phases of movements. We have previously examined the neural mechanisms underlying AO and MI of walking, focusing on the combined effect of AO with MI (AO+MI) and phase dependent modulation of corticospinal and spinal reflex excitability. Here, as a continuation of our previous studies, we investigated cortical activity depending on gait phases during AO and AO+MI of walking by using electroencephalography (EEG); 64-channel EEG signals were recorded in which participants observed walking with or without imagining it, respectively. EEG source and spectral analyses showed that, in the sensorimotor cortex during AO+MI and AO, the alpha and beta power were decreased, and power spectral modulations depended on walking phases. The phase dependent modulations during AO+MI, but not during AO, were like those which occur during actual walking as reported by previous walking studies. These results suggest that combinatory effects of AO+MI could induce parts of the phase dependent activation of the sensorimotor cortex during walking even without any movements. These findings would extend understanding of the neural mechanisms underlying walking and cognitive motor processes and provide clinically beneficial information towards rehabilitation for patients with neurological gait dysfunctions.

Rhythmic neural activity is comodulated with short-term gait modifications during first-time use of a dummy prosthesis: a pilot study

Background

After transfemoral amputation, many hours of practice are needed to re-learn walking with a prosthesis. The long adaptation process that consolidates a novel gait pattern seems to depend on cerebellar function for reinforcement of specific gait modifications, but the precise, step-by-step gait modifications (e.g., foot placement) most likely rely on top-down commands from the brainstem and cerebral cortex. The aim of this study was to identify, in able-bodied individuals, the specific modulations of cortical rhythms that accompany short-term gait modifications during first-time use of a dummy prosthesis.

Methods

Fourteen naïve participants walked on a treadmill without (one block, 4 min) and with a dummy prosthesis (three blocks, 3 × 4 min), while ground reaction forces and 32-channel EEG were recorded. Gait cycle duration, stance phase duration, step width, maximal ground reaction force and, ground reaction force trace over time were measured to identify gait modifications. Independent component analysis of EEG data isolated brain-related activity from distinct anatomical sources. The source-level data were segmented into gait cycles and analyzed in the time–frequency domain to reveal relative enhancement or suppression of intrinsic cortical oscillations. Differences between walking conditions were evaluated with one-way ANOVA and post-hoc testing (α = 0.05).

Results

Immediate modifications occurred in the gait parameters when participants were introduced to the dummy prosthesis. Except for gait cycle duration, these modifications remained throughout the duration of the experimental session. Power modulations of the theta, mu, beta, and gamma rhythms, of sources presumably from the fronto-central and the parietal cortices, were found across the experimental session. Significant power modulations of the theta, beta, and gamma rhythms within the gait cycle were predominately found around the heel strike of both feet and the swing phase of the right (prosthetic) leg.

Conclusions

The modulations of cortical activity could be related to whole-body coordination, including the swing phase and placing of the prosthesis, and the bodyweight transfer between legs and arms. Reduced power modulation of the gamma rhythm within the experimental session may indicate initial motor memories being formed. Better understanding of the sensorimotor processes behind gait modifications may inform the development of neurofeedback strategies to assist gait rehabilitation.

Defective corticomuscular connectivity during walking in patients with Parkinson's disease

Gait disturbances are common in individuals with Parkinson's disease (PD). Although the basic patterns of walking are thought to be controlled by the brainstem and spinal networks, recent studies have found significant corticomuscular coherence in healthy individuals during walking. However, it still remains unknown how PD affects the cortical control of muscles during walking. As PD typically develops in older adults, it is important to investigate the effects of both aging and PD when examining disorders in patients with PD. Here, we assessed the effects of PD and aging on corticomuscular communication during walking by investigating corticomuscular coherence. We recorded electroencephalographic and electromyographic signals in 10 individuals with PD, 9 healthy older individuals, and 15 healthy young individuals. We assessed the corticomuscular coherence between the motor cortex and two lower leg muscles, tibialis anterior (TA) and medial gastrocnemius, during walking. Older and young groups showed sharp peaks in muscle activation patterns at specific gait phases, whereas the PD group showed prolonged patterns. Smaller corticomuscular coherence was found in the PD group compared with the healthy older group in the α band (8-12 Hz) for both muscles, and in the β band (16-32 Hz) for TA. Older and young groups did not differ in the magnitude of corticomuscular coherence. Our results indicated that PD decreased the corticomuscular coherence during walking, whereas it was not affected by aging. This lower corticomuscular coherence in PD may indicate lower-than-normal corticomuscular communication, although direct or indirect communication is unknown, and may cause impaired muscle control during walking.NEW & NOTEWORTHY Mechanisms behind how Parkinson's disease (PD) affects cortical control of muscles during walking remain unclear. As PD typically develops in the elderly, investigation of aging effects is important to examine deficits regarding PD. Here, we demonstrated that PD causes weak corticomuscular synchronization during walking, but aging does not. This lower-than-normal corticomuscular communication may cause impaired muscle control during walking.

A walk in the park? Characterizing gait-related artifacts in mobile EEG recordings

Brain activity during natural walking outdoors can be captured using mobile electroencephalography (EEG). However, EEG recorded during gait is confounded with artifacts from various sources, possibly obstructing the interpretation of brain activity patterns. Currently, there is no consensus on how the amount of artifact present in these recordings should be quantified, or is there a systematic description of gait artifact properties. In the current study, we expand several features into a seven-dimensional footprint of gait-related artifacts, combining features of time, time-frequency, spatial, and source domains. EEG of N = 26 participants was recorded while standing and walking outdoors. Footprints of gait-related artifacts before and after two different artifact attenuation strategies (after artifact subspace reconstruction (ASR) and after subsequent independent component analysis [ICA]) were systematically different. We also evaluated topographies, morphologies, and signal-to-noise ratios (SNR) of button-press event-related potentials (ERP) before and after artifact handling, to confirm gait-artifact reduction specificity. Morphologies and SNR remained unchanged after artifact attenuation, whereas topographies improved in quality. Our results show that the footprint can provide a detailed assessment of gait-related artifacts and can be used to estimate the sensitivity of different artifact reduction strategies. Moreover, the analysis of button-press ERPs demonstrated its specificity, as processing did not only reduce gait-related artifacts but ERPs of interest remained largely unchanged. We conclude that the proposed footprint is well suited to characterize individual differences in gait-related artifact extent. In the future, it could be used to compare and optimize recording setups and processing pipelines comprehensively.

Improved Gait of Persons With Multiple Sclerosis After Rehabilitation: Effects on Lower Limb Muscle Synergies, Push-Off, and Toe-Clearance

Introduction: Persons with MS (PwMS) have markedly reduced push-off and toe-clearance during gait compared to healthy subjects (HS). These deficits may result from alterations in neuromotor control at the ankle. To optimize rehabilitation interventions for PwMS, a crucial step is to evaluate if and how altered neuromotor control, as represented by muscle synergies, improves with rehabilitation. In this study we investigated changes in ankle motor control and associated biomechanical parameters during gait in PwMS, occurring with increase in speed after gait rehabilitation. Methods: 3D motion and EMG data were collected while 11 PwMS (age 50.3 + 11.1; EDSS 5.2 + 1.2) walked overground at self-selected speed before (T0) and after 20 sessions (T1) of intensive treadmill training. Muscle synergies were extracted using non-negative matrix factorization. Gait parameters were computed according to the LAMB protocol. Pearson's correlation coefficient was used to evaluate the similarity of motor modules between PwMS and HS. To assess differences in distal module activations representing neuromotor control at the ankle [Forward Propulsion (FPM) and Ground Clearance modules (GCM)], each module's activation timing was integrated over 100% of the gait cycle and the activation percentage index (API) was computed in six phases. Ten age matched HS provided two separate speed-matched normative datasets for T0 and T1. For speed independent comparison for the PwMs Z scores were calculated for all their gait variables. Results: In PwMS velocity increased significantly from T0 to T1 (0.74-0.90 m/s, p < 0.05). The activation profiles (API) of FPM and GCM of PwMS improved in pre-swing (p < 0.05): FPM (Mean [95% CI] [%]: T0: 12.5 [5.7-19.3] vs. T1: 9.0 [2.7-15.3]); GCM (T0: 26.7 [18.2-35.3] vs. T1: 24.5 [18.2-30.7]). This was associated with an increase in toe clearance (80.3 to 103.6 mm, p < 0.05) and a higher ankle power peak in pre-swing (1.53-1.93 W/kg, p < 0.05). Conclusion: Increased gait speed of PwMS after intensive gait training was consistent with improvements in spatio-temporal gait parameters. The most important finding of this study was the re-organization of distal leg modules related to neurophysiological changes induced by rehabilitation. This was associated with an improved ankle performance.

Motion and Muscle Artifact Removal Validation Using an Electrical Head Phantom, Robotic Motion Platform, and Dual Layer Mobile EEG

Motion and muscle artifacts can undermine signal quality in electroencephalography (EEG) recordings during locomotion. We evaluated approaches for recovering ground-truth artificial brain signals from noisy EEG recordings. We built an electrical head phantom that broadcast four brain and four muscle sources. Head movements were generated by a robotic motion platform. We recorded 128-channel dual layer EEG and 8-channel neck electromyography (EMG) from the head phantom during motion. We evaluated ground-truth electrocortical source signal recovery from artifact contaminated data using Independent Component Analysis (ICA) to determine: (1) the number of isolated noise sensor recordings needed to capture and remove motion artifacts, (2) the ability of Artifact Subspace Reconstruction to remove motion and muscle artifacts at contrasting artifact detection thresholds, (3) the number of neck EMG sensor recordings needed to capture and remove muscle artifacts, and (4) the ability of Canonical Correlation Analysis to remove muscle artifacts. We also evaluated source signal recovery by combining the best practices identified in aims 1-4. By including isolated noise and EMG recordings in the ICA decomposition, we more effectively recovered ground-truth artificial brain signals. A reduced subset of 32-noise and 6-EMG channels showed equivalent performance compared to including the complete arrays. Artifact Subspace Reconstruction improved source separation, but this was contingent on muscle activity amplitude. Canonical Correlation Analysis also improved source separation. Merging noise and EMG recordings into the ICA decomposition, with Artifact Subspace Reconstruction and Canonical Correlation Analysis preprocessing, improved source signal recovery. This study expands on previous head phantom experiments by including neck muscle source activity and evaluating artificial electrocortical spectral power fluctuations synchronized with gait events.

Older adults reduce the complexity and efficiency of neuromuscular control to preserve walking balance

Healthy aging modifies neuromuscular control of dynamic balance. Challenging tasks could amplify such modifications, providing clinical insights. We examined the effects of age and walking condition difficulty on neuromuscular control of walking balance. We analyzed whole-body kinematics and activity of 13 right leg and trunk muscles in 17 young (11 males and 6 females; age 24 ± 3 years) and 14 older adults (3 males and 11 females; age 69 ± 4 years) while walking on a taped line on the floor and a 6-cm wide beam. Spatiotemporal parameters of gait, margin of stability, motor performance, and muscle synergies were estimated. Regardless of age, maintaining walking balance was more difficult on the beam compared to the taped line as evidenced by a shorter distance walked (17.3%), a reduction in step length (5.8%) and speed (10.3%), as well as a 40.0% smaller margin of stability during beam vs. tape walking. The number of muscle synergies was also higher during beam vs. tape walking. Compared to younger adults, older adults had larger margin of stability during beam walking. Older adults also had higher muscle co-activity within each muscle synergy and greater variance accounted for by the first muscle synergy regardless of condition. Such age-effects may be interpreted as a safer, less efficient, and less complex neuromuscular modular control strategy. In conclusion, beam walking increased the difficulty of maintaining walking balance and induced adaptations in modular control. It seems that healthy older adults reduce the complexity and efficiency of neuromuscular control of walking to preserve walking balance.

Gait-combined transcranial alternating current stimulation modulates cortical control of muscle activities during gait

Non-invasive brain stimulation has been of interest as a therapeutic tool to modulate cortical excitability. However, there is little evidence that oscillatory brain stimulation can modulate the cortical control of muscle activities during gait, which can be assessed using coherence analysis of paired surface electromyographic (EMG) recordings. This study aimed to investigate the effects of gait-combined transcranial alternating current stimulation (tACS) at the gait cycle frequency on the cortical control of muscle activities during gait using EMG-EMG coherence analysis. Fourteen healthy young adults participated in this study. All participants underwent 2 test conditions (real tACS and sham stimulation over the leg area of the primary motor cortex during 10-min treadmill walking). The average peak-to-peak amplitudes of the motor evoked potentials (MEPs) from the tibialis anterior (TA) and lateral gastrocnemius muscles in the sitting position and EMG-EMG coherences in the TA muscle, triceps surae muscles, quadriceps muscles, and hamstring muscles during gait were measured before and after stimulation. Entrainment effect was significantly higher during real tACS than during sham stimulation. After real tACS, the MEP amplitude and beta band (13-33 Hz) coherence area increased in the TA muscle. The change in MEP amplitude from the TA muscle was positively correlated with the change in beta band coherence area in the TA muscle. Gait-combined tACS can modulate the strength of descending neural drive to TA motoneurons during gait. This suggests that oscillatory brain stimulation is a useful therapeutic tool to modulate the cortical control of muscle activities during gait.

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

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

Gait-phase-dependent and gait-phase-independent cortical activity across multiple regions involved in voluntary gait modifications in humans

Modification of ongoing walking movement to fit changes in external environments requires accurate voluntary control. In cats, the motor and posterior parietal cortices have crucial roles for precisely adjusting limb trajectory during walking. In human walking, however, it remains unclear which cortical information contributes to voluntary gait modification. In this study, we investigated cortical activity changes associated with visually guided precision stepping using electroencephalography source analysis. Our results demonstrated frequency- and gait-event-dependent changes in the cortical power spectrum elicited by voluntary gait modification. The main differences between normal walking and precision stepping were as follows: (a) the alpha, beta or gamma power decrease during the swing phases in the sensorimotor, anterior cingulate and parieto-occipital cortices, and (b) a power decrease in the theta, alpha and beta bands and increase in the gamma band throughout the gait cycle in the parieto-occipital cortex. Based on the previous knowledge of brain functions, the former change was considered to be related to execution and planning of leg movement, while the latter change was considered to be related to multisensory integration and motor awareness. Therefore, our results suggest that the gait modification is achieved by higher cortical involvements associated with different sensorimotor-related functions across multiple cortical regions including the sensorimotor, anterior cingulate and parieto-occipital cortices. The results imply the critical importance of the cortical contribution to voluntary modification in human locomotion. Further, the observed cortical information related to voluntary gait modification would contribute to developing volitional control systems of brain-machine interfaces for walking rehabilitation.

Dynamics and coupling of fractional-order models of the motor cortex and central pattern generators

OBJECTIVE

Fractional calculus plays a key role in the analysis of neural dynamics. In particular, fractional calculus has been recently exploited for analyzing complex biological systems and capturing intrinsic phenomena. Also, artificial neural networks have been shown to have complex neuronal dynamics and characteristics that can be modeled by fractional calculus. Moreover, for a neural microcircuit placed on the spinal cord, fractional calculus can be employed to model the central pattern generator (CPG). However, the relation between the CPG and the motor cortex is still unclear.

APPROACH

In this paper, fractional-order models of the CPG and the motor cortex are built on the Van der Pol oscillator and the neural mass model (NMM), respectively. A self-consistent mean field approximation is used to construct the potential landscape of the Van der Pol oscillator. This landscape provides a useful tool to observe the 3D dynamics of the oscillator. To infer the relation of the motor cortex and CPG, the coupling model between the fractional-order Van der Pol oscillator and the NMM is built. As well, the influence of the coupling parameters on the CPG and the motor cortex is assessed.

MAIN RESULTS

Fractional-order NMM and coupling model of the motor cortex and the CPG are first established. The potential landscape is used to show 3D probabilistic evolution of the Van der Pol oscillator states. Detailed observations of the evolution of the system states can be made with fractional calculus. In particular, fractional calculus enables the observation of the creation of stable modes and switching between them.

SIGNIFICANCE

The results confirm that the motor cortex and CPG have associated modes or states that can be switched based on changes in the fractional order and the time delay. Fractional calculus and the potential landscape are helpful methods for better understanding of the working principles of locomotion systems.

Adaptive trunk sway velocities following repeated perturbations in subjects with and without low back pain

Faster trunk motions could be a strategy to prevent loss of balance and fall injuries due to unexpected perturbations. However, it is unclear how trunk sway velocities can be compensated during stepping in subjects with low back pain (LBP). The purpose of this study was to investigate lower limb reaction, swing, and step times, as well as trunk sway velocities at heel strike and toe-off, following repeated step perturbations between subjects with and without LBP. There were 30 subjects with LBP and 42 control subjects who were exposed to treadmill-induced perturbations at a velocity of 0.12 m/sec for 0.62 m. The treadmill-induced steps caused subjects to walk forward for 4.90 sec after the perturbation. The groups demonstrated significant interactions on the lower limb reaction times and on the number of repeated perturbations (F = 4.83, p = 0.03) due to a decreased step time at the first perturbation (t = 2.52, p = 0.01) in the LBP group. For the trunk sway velocities, the repeated perturbations demonstrated a significant interaction between groups (F = 4.65, p = 0.03). This adaptive trunk strategy for gait stability increased step times with repeated perturbations in the LBP group. The group interactions on the trunk sway velocities also indicated a possible somatosensory integration for step time adjustments to avoid potential fall hazards. This adaptive response with repeated step perturbations could result in compensatory trunk sway for gait stability.

Corticomuscular interactions during different movement periods in a multi-joint compound movement

While much is known about motor control during simple movements, corticomuscular communication profiles during compound movement control remain largely unexplored. Here, we aimed at examining frequency band related interactions between brain and muscles during different movement periods of a bipedal squat (BpS) task utilizing regression corticomuscular coherence (rCMC), as well as partial directed coherence (PDC) analyses. Participants performed 40 squats, divided into three successive movement periods (Eccentric (ECC), Isometric (ISO) and Concentric (CON)) in a standardized manner. EEG was recorded from 32 channels specifically-tailored to cover bilateral sensorimotor areas while bilateral EMG was recorded from four main muscles of BpS. We found both significant CMC and PDC (in beta and gamma bands) during BpS execution, where CMC was significantly elevated during ECC and CON when compared to ISO. Further, the dominant direction of information flow (DIF) was most prominent in EEG-EMG direction for CON and EMG-EEG direction for ECC. Collectively, we provide novel evidence that motor control during BpS is potentially achieved through central motor commands driven by a combination of directed inputs spanning across multiple frequency bands. These results serve as an important step toward a better understanding of brain-muscle relationships during multi joint compound movements.

Corticospinal Control of Human Locomotion as a New Determinant of Age-Related Sarcopenia: An Exploratory Study

Sarcopenia is a muscle disease listed within the ICD-10 classification. Several operational definitions have been created for sarcopenia screening; however, an international consensus is lacking. The Centers for Disease Control and Prevention have recently recognized that sarcopenia detection requires improved diagnosis and screening measures. Mounting evidence hints towards changes in the corticospinal communication system where corticomuscular coherence (CMC) reflects an effective mechanism of corticospinal interaction. CMC can be assessed during locomotion by means of simultaneously measuring Electroencephalography (EEG) and Electromyography (EMG). The aim of this study was to perform sarcopenia screening in community-dwelling older adults and explore the possibility of using CMC assessed during gait to discriminate between sarcopenic and non-sarcopenic older adults. Receiver Operating Characteristic (ROC) curves showed high sensitivity, precision and accuracy of CMC assessed from EEG Cz sensor and EMG sensors located over Musculus Vastus Medialis [Cz-VM; AUC (95.0%CI): 0.98 (0.92-1.04), sensitivity: 1.00, 1-specificity: 0.89, p < 0.001] and with Musculus Biceps Femoris [Cz-BF; AUC (95.0%CI): 0.86 (0.68-1.03), sensitivity: 1.00, 1-specificity: 0.70, p < 0.001]. These muscles showed significant differences with large magnitude of effect between sarcopenic and non-sarcopenic older adults [Hedge's g (95.0%CI): 2.2 (1.3-3.1), p = 0.005 and Hedge's g (95.0%CI): 1.5 (0.7-2.2), p = 0.010; respectively]. The novelty of this exploratory investigation is the hint toward a novel possible determinant of age-related sarcopenia, derived from corticospinal control of locomotion and shown by the observed large differences in CMC when sarcopenic and non-sarcopenic older adults are compared. This, in turn, might represent in future a potential treatment target to counteract sarcopenia as well as a parameter to monitor the progression of the disease and/or the potential recovery following other treatment interventions.

Faster Gait Speeds Reduce Alpha and Beta EEG Spectral Power From Human Sensorimotor Cortex

OBJECTIVE

Our aim was to determine if walking speed affected human sensorimotor electrocortical dynamics using mobile high-density electroencephalography (EEG).

METHODS

To overcome limitations associated with motion and muscle artifact contamination in EEG recordings, we compared solutions for artifact removal using novel dual-layer EEG electrodes and alternative signal processing methods. Dual-layer EEG simultaneously recorded human electrocortical signals and isolated motion artifacts using pairs of mechanically coupled and electrically independent electrodes. For electrical muscle activity removal, we incorporated electromyographic (EMG) recordings from the neck into our mobile EEG data processing pipeline. We compared artifact removal methods during treadmill walking at four speeds (0.5, 1.0, 1.5, and 2.0 m/s).

RESULTS

Left and right sensorimotor alpha and beta spectral power increased in contralateral limb single support and push off, and decreased during contralateral limb swing at each speed. At faster walking speeds, sensorimotor spectral power fluctuations were less pronounced across the gait cycle with reduced alpha and beta power (p < 0.05) compared to slower speeds. Isolated noise recordings and neck EMG spectral power fluctuations matched gait events and showed broadband spectral power increases at faster speeds.

CONCLUSION AND SIGNIFICANCE

Dual-layer EEG enabled us to isolate changes in human sensorimotor electrocortical dynamics across walking speeds. A comparison of signal processing approaches revealed similar intrastride cortical fluctuations when applying common (e.g., artifact subspace reconstruction) and novel artifact rejection methods. Dual-layer EEG, however, allowed us to document and rule out residual artifacts, which exposed sensorimotor spectral power changes across gait speeds.

Corticomuscular control of walking in older people and people with Parkinson's disease

Changes in human gait resulting from ageing or neurodegenerative diseases are multifactorial. Here we assess the effects of age and Parkinson’s disease (PD) on corticospinal activity recorded during treadmill and overground walking. Electroencephalography (EEG) from 10 electrodes and electromyography (EMG) from bilateral tibialis anterior muscles were acquired from 22 healthy young, 24 healthy older and 20 adults with PD. Event-related power, corticomuscular coherence (CMC) and inter-trial coherence were assessed for EEG from bilateral sensorimotor cortices and EMG during the double-support phase of the gait cycle. CMC and EMG power at low beta frequencies (13–21 Hz) was significantly decreased in older and PD participants compared to young people, but there was no difference between older and PD groups. Older and PD participants spent shorter time in the swing phase than young individuals. These findings indicate age-related changes in the temporal coordination of gait. The decrease in low-beta CMC suggests reduced cortical input to spinal motor neurons in older people during the double-support phase. We also observed multiple changes in electrophysiological measures at low-gamma frequencies during treadmill compared to overground walking, indicating task-dependent differences in corticospinal locomotor control. These findings may be affected by artefacts and should be interpreted with caution.

Brain Functional Connectivity in Unconstrained Walking With and Without an Exoskeleton

An exoskeleton is utilized to effectively restore the motor function of amputees' limbs and is frequently employed in motor rehabilitation training during convalescence. Understanding of exoskeleton impact on the brain is required in order to better and more efficiently use the exoskeleton. Almost all previous studies investigated the exoskeleton effect on the brain in a situation with constraints such as predefined walking speed, which could lead to findings differed from that obtained in an unconstrained situation. We, therefore, performed an experiment of unconstrained walking with and without an exoskeleton. Both individual connections and graph metrics were explored and compared among walking conditions. We found that low-order functional connections and associated high-order functional connections mainly between the left centroparietal region and right frontal region were significantly different among walking conditions. Generally speaking, connective strength was enhanced in LOFC and was decreased in aHOFC when assistant force was provided by the exoskeleton. Further, we proposed connection length investigation and revealed the large majority of these connections were long-distance connectivity. Graph metric investigation discovered higher connectivity clustering in the walking with low exoskeleton-aided force compared to the walking without the exoskeleton. This study expanded the existing knowledge of the effect of exoskeleton on the brain and is of implications on new exoskeleton development and exoskeleton-aided rehabilitation training.

Cortical reactive balance responses to unexpected slippages while walking: a pilot study

Understanding how the human brain cortex behaves when the dynamical balance is unexpectedly challenged can be useful to enable fall prevention strategies during daily activities. In this respect, we designed and tested a novel methodological approach to early detect modifications of the scalp-level signals when steady walking is perturbed. Four young adults were asked to manage unexpected bilateral slippages while steadily walking at their self-selected speed. Lower limb kinematics, electromyographic (EMG) and electroencephalographic (EEG; 13 channels from motor and sensory-motor cortex areas) signals were synchronously recorded. EMG signals from Vastus Medialis (both sides) were used to trigger the analysis of the EEG before and after the perturbation onset. Cortical activity was then assessed and compared pre vs. post perturbation. Specifically, for each gait cycle, the rate of variation of the EEG power spectrum density, named m, was used to describe the cortical responsiveness in five bands of interests: ϑ (4-7 Hz), α (8-12 Hz), β I, β II, β III rhythms (13-15, 15-20, 18-28 Hz). Results revealed a sharp increment of m early after the onset of the perturbation (perturbed step) compared to steady locomotion, for all rhythms. This cortical behavior disappeared during the recovery step. This study promisingly supports the evidence that the proposed approach can distinguish between steady walking and early reactive balance recovery, paving the way for the EEG-based monitoring of the fall risk during daily activities.

Pre-Movement Cortico-Muscular Dynamics Underlying Improved Parkinson Gait Initiation after Instructed Arm Swing

BACKGROUND

The supplementary motor area (SMA) is implicated in both motor initiation and stereotypic multi-limb movements such as walking with arm swing. Gait in Parkinson's disease exhibits starting difficulties and reduced arm swing, consistent with reduced SMA activity.

OBJECTIVE

We tested whether enhanced arm swing could improve Parkinson gait initiation and assessed whether increased SMA activity during preparation might facilitate such improvement.

METHODS

Effects of instructed arm swing on cortical activity, muscle activity and kinematics were assessed by ambulant EEG, EMG, accelerometers and video in 17 Parkinson patients and 19 controls. At baseline, all participants repeatedly started walking after a simple auditory cue. Next, patients started walking at this cue, which now meant starting with enhanced arm swing. EEG changes over the putative SMA and leg motor cortex were assessed by event related spectral perturbation (ERSP) analysis of recordings at Fz and Cz.

RESULTS

Over the putative SMA location (Fz), natural PD gait initiation showed enhanced alpha/theta synchronization around the auditory cue, and reduced alpha/beta desynchronization during gait preparation and movement onset, compared to controls. Leg muscle activity in patients was reduced during preparation and movement onset, while the latter was delayed compared to controls. When starting with enhanced arm swing, these group differences virtually disappeared.

CONCLUSION

Instructed arm swing improves Parkinson gait initiation. ERSP normalization around the cue indicates that the attributed information may serve as a semi-internal cue, recruiting an internalized motor program to overcome initiation difficulties.

Effects of supraspinal feedback on human gait: rhythmic auditory distortion

BACKGROUND

Different types of sound cues have been used to adapt the human gait rhythm. We investigated whether young healthy volunteers followed subliminal metronome rhythm changes during gait.

METHODS

Twenty-two healthy adults walked at constant speed on a treadmill following a metronome sound cue (period 566 msec). The metronome rhythm was then either increased or decreased, without informing the subjects, at 1 msec increments or decrements to reach, respectively, a low (596 msec) or a high frequency (536 msec) plateaus. After 30 steps at one of these isochronous conditions, the rhythm returned to the original period with decrements or increments of 1 msec. Motion data were recorded with an optical measurement system to determine footfall. The relative phase between sound cue (stimulus) and foot contact (response) were compared.

RESULTS

Gait was entrained to the rhythmic auditory stimulus and subjects subconsciously adapted the step time and length to maintain treadmill speed, while following the rhythm changes. In most cases there was a lead error: the foot contact occurred before the sound cue. The mean error or the absolute mean relative phase increased during the isochronous high (536 msec) or low frequencies (596 msec).

CONCLUSION

These results showed that the gait period is strongly "entrained" with the first metronome rhythm while subjects still followed metronome changes with larger error. This suggests two processes: one slow-adapting, supraspinal oscillator with persistence that predicts the foot contact to occur ahead of the stimulus, and a second fast process linked to sensory inputs that adapts to the mismatch between peripheral sensory input (foot contact) and supraspinal sensory input (auditory rhythm).

Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance

This study expores neural activity underlying creative processes through the investigation of music improvisation. Fourteen guitar players with a high level of improvisation skill participated in this experiment. The experimental task involved playing 32-s alternating blocks of improvisation and scales on guitar. electroencephalography (EEG) data was measured continuously throughout the experiment. In order to remove potential artifacts and extract brain-related activity the following signal processing techniques were employed: bandpass filtering, Artifact Subspace Reconstruction, and Independent Component Analysis (ICA). For each participant, artifact related independent components (ICs) were removed from the EEG data and only ICs found to be from brain activity were retained. Source localization using this brain-related activity was carried out using sLORETA. Greater activity for improvisation over scale was found in multiple frequency bands (theta, alpha, and beta) localized primarily in the medial frontal cortex (MFC), Middle frontal gyrus (MFG), anterior cingulate, polar medial prefrontal cortex (MPFC), premotor cortex (PMC), pre and postcentral gyrus (PreCG and PostCG), superior temporal gyrus (STG), inferior parietal lobule (IPL), and the temporal-parietal junction. Together this collection of brain regions suggests that improvisation was mediated by processes involved in coordinating planned sequences of movement that are modulated in response to ongoing environmental context through monitoring and feedback of sensory states in relation to internal plans and goals. Machine-learning using Common Spatial Patterns (CSP) for EEG feature extraction attained a mean of over 75% classification performance for improvisation vs. scale conditions across participants. These machine-learning results are a step towards the development of a brain-computer interface that could be used for neurofeedback training to improve creativity.

Muscle-Specific Modulation of Spinal Reflexes in Lower-Limb Muscles during Action Observation with and without Motor Imagery of Walking

Action observation (AO) and motor imagery (MI) are useful techniques in neurorehabilitation. Previous studies have reported that AO and MI facilitate corticospinal excitability only in those muscles that are active when actually performing the observed or imagined movements. However, it remained unclear whether spinal reflexes modulate multiple muscles simultaneously. The present study focused on AO and MI of walking and aimed to clarify their effects on spinal reflexes in lower-limb muscles that are recruited during actual walking. Ten healthy males participated in the present study. Spinal reflex parameters evoked by transcutaneous spinal cord stimulation were measured from five lower-limb muscles during rest, AO, and AO combined with MI (AO + MI) conditions. Our results showed that spinal reflexes were increased in the tibialis anterior and biceps femoris muscles during AO and in the tibialis anterior, soleus, and medial gastrocnemius muscles during AO + MI, compared with resting condition. Spinal reflex parameters in the vastus medialis muscle were unchanged. These results indicate the muscle-specific modulations of spinal reflexes during AO and AO + MI. These findings reveal the underlying neural activities induced by AO, MI, and their combined processes.

Pathway-specific modulatory effects of neuromuscular electrical stimulation during pedaling in chronic stroke survivors

Background

Neuromuscular electrical stimulation (NMES) is extensively used in stroke motor rehabilitation. How it promotes motor recovery remains only partially understood. NMES could change muscular properties, produce altered sensory inputs, and modulate fluctuations of cortical activities; but the potential contribution from cortico-muscular couplings during NMES synchronized with dynamic movement has rarely been discussed.

Method

We investigated cortico-muscular interactions during passive, active, and NMES rhythmic pedaling in healthy subjects and chronic stroke survivors. EEG (128 channels), EMG (4 unilateral lower limb muscles) and movement parameters were measured during 3 sessions of constant-speed pedaling. Sensory-level NMES (20 mA) was applied to the muscles, and cyclic stimulation patterns were synchronized with the EMG during pedaling cycles. Adaptive mixture independent component analysis was utilized to determine the movement-related electro-cortical sources and the source dipole clusters. A directed cortico-muscular coupling analysis was conducted between representative source clusters and the EMGs using generalized partial directed coherence (GPDC). The bidirectional GPDC was compared across muscles and pedaling sessions for post-stroke and healthy subjects.

Results

Directed cortico-muscular coupling of NMES cycling was more similar to that of active pedaling than to that of passive pedaling for the tested muscles. For healthy subjects, sensory-level NMES could modulate GPDC of both ascending and descending pathways. Whereas for stroke survivors, NMES could modulate GPDC of only the ascending pathways.

Conclusions

By clarifying how NMES influences neuromuscular control during pedaling in healthy and post-stroke subjects, our results indicate the potential limitation of sensory-level NMES in promoting sensorimotor recovery in chronic stroke survivors.

Challenge for Affective Brain-Computer Interfaces: Non-stationary Spatio-spectral EEG Oscillations of Emotional Responses

Electroencephalogram (EEG)-based affective brain-computer interfaces (aBCIs) have been attracting ever-growing interest and research resources. Whereas most previous neuroscience studies have focused on single-day/-session recording and sensor-level analysis, less effort has been invested in assessing the fundamental nature of non-stationary EEG oscillations underlying emotional responses across days and individuals. This work thus aimed to use a data-driven blind source separation method, i.e., independent component analysis (ICA), to derive emotion-relevant spatio-spectral EEG source oscillations and assess the extent of non-stationarity. To this end, this work conducted an 8-day music-listening experiment (i.e., roughly interspaced over 2 months) and recorded whole-scalp 30-ch EEG data from 10 subjects. Given the large size of the data (i.e., from 80 sessions), results indicated that EEG non-stationarity was clearly revealed in the numbers and locations of brain sources of interest as well as their spectral modulation to the emotional responses. Less than half of subjects (two to four) showed the same relatively day-stationary (source reproducibility >6 days) spatio-spectral tendency towards one of the binary valence and arousal states. This work substantially advances the previous work by exploiting intra- and inter-individual EEG variability in an ecological multiday scenario. Such EEG non-stationarity may inevitably present a great challenge for the development of an accurate, robust, and generalized emotion-classification model.

Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal

One of the greatest challenges that hinder the decoding and application of electroencephalography (EEG) is that EEG recordings almost always contain artifacts - non-brain signals. Among existing automatic artifact-removal methods, artifact subspace reconstruction (ASR) is an online and realtime capable, component-based method that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameter have not been evaluated and reported, especially on real EEG data. This study systematically validates ASR on ten EEG recordings in a simulated driving experiment. Independent component analysis (ICA) is applied to separate artifacts from brain signals to allow a quantitative assessment of ASR's effectiveness in removing various types of artifacts and preserving brain activities. Empirical results show that the optimal ASR parameter is between 10 and 100, which is small enough to remove activities from artifacts and eye-related components and large enough to retain signals from brain-related components. With the appropriate choice of the parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.

High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task

In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.

Gait-Synchronized Rhythmic Brain Stimulation Improves Poststroke Gait Disturbance: A Pilot Study

Background and Purpose- Gait disturbance is one of serious impairments lowering activity of daily life in poststroke patients. The patients often show reduced hip and knee joint flexion and ankle dorsiflexion of the lower limbs during the swing phase of gait, which is partly controlled by the primary motor cortex (M1). In the present study, we investigated whether gait-synchronized rhythmic brain stimulation targeting swing phase-related M1 activity can improve gait function in poststroke patients. Methods- Eleven poststroke patients in the chronic phase participated in this single-blind crossover study. Each patient received oscillatory transcranial direct current stimulation over the affected M1 foot area and sham stimulation during treadmill gait. The brain stimulation was synchronized with individual gait rhythm, and the electrical current peaks reached immediately before initiation of the swing phase of the paretic lower limb. Ankle dorsiflexion was assisted by electrical neuromuscular stimulation in both real and sham conditions. Results- Regarding the effects of a single intervention, the speed of self-paced gait was significantly increased after oscillatory transcranial direct current stimulation, but not after sham stimulation (paired t test, P=0.009). After we administered the intervention repeatedly, self- and maximally paced gait speed and timed up and go test performance were significantly improved (self-paced: F_(1,21)=8.91, P=0.007, maximally paced: F_(1,21)=7.09, P=0.015 and timed up and go test: F_(1,21)=12.27, P=0.002), along with improved balance function and increased joint flexion of the paretic limbs during gait. Conclusions- These findings suggest that rhythmic brain stimulation synchronized with gait rhythm might be a promising approach to induce gait recovery in poststroke patients. Clinical Trial Registration- URL: https://www.umin.ac.jp/ctr/. Unique identifier: UMIN000013676.

Control of human gait stability through foot placement

During human walking, the centre of mass (CoM) is outside the base of support for most of the time, which poses a challenge to stabilizing the gait pattern. Nevertheless, most of us are able to walk without substantial problems. In this review, we aim to provide an integrative overview of how humans cope with an underactuated gait pattern. A central idea that emerges from the literature is that foot placement is crucial in maintaining a stable gait pattern. In this review, we explore this idea; we first describe mechanical models and concepts that have been used to predict how foot placement can be used to control gait stability. These concepts, such as for instance the extrapolated CoM concept, the foot placement estimator concept and the capture point concept, provide explicit predictions on where to place the foot relative to the body at each step, such that gait is stabilized. Next, we describe empirical findings on foot placement during human gait in unperturbed and perturbed conditions. We conclude that humans show behaviour that is largely in accordance with the aforementioned concepts, with foot placement being actively coordinated to body CoM kinematics during the preceding step. In this section, we also address the requirements for such control in terms of the sensory information and the motor strategies that can implement such control, as well as the parts of the central nervous system that may be involved. We show that visual, vestibular and proprioceptive information contribute to estimation of the state of the CoM. Foot placement is adjusted to variations in CoM state mainly by modulation of hip abductor muscle activity during the swing phase of gait, and this process appears to be under spinal and supraspinal, including cortical, control. We conclude with a description of how control of foot placement can be impaired in humans, using ageing as a primary example and with some reference to pathology, and we address alternative strategies available to stabilize gait, which include modulation of ankle moments in the stance leg and changes in body angular momentum, such as rapid trunk tilts. Finally, for future research, we believe that especially the integration of consideration of environmental constraints on foot placement with balance control deserves attention.

Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings

OBJECTIVE

Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported, especially on actual EEG data.

METHODS

This paper systematically evaluates ASR on 20 EEG recordings taken during simulated driving experiments. Independent component analysis (ICA) and an independent component classifier are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of the ASR.

RESULTS

ASR removes more eye and muscle components than brain components. Even though some eye and muscle components retain after ASR cleaning, the power of their temporal activities is reduced. Study results also showed that ASR cleaning improved the quality of a subsequent ICA decomposition.

CONCLUSIONS

Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities.

SIGNIFICANCE

With an appropriate choice of parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.

Group-level cortical and muscular connectivity during perturbations to walking and standing balance

Maintaining balance is a complex process requiring multisensory processing and coordinated muscle activation. Previous studies have indicated that the cortex is directly involved in balance control, but less information is known about cortical flow of signals for balance. We studied source-localized electrocortical effective connectivity dynamics of healthy young subjects (29 subjects: 14 male and 15 female) walking and standing with both visual and physical perturbations to their balance. The goal of this study was to quantify differences in group-level corticomuscular connectivity responses to sensorimotor perturbations during walking and standing. We hypothesized that perturbed visual input during balance would transiently decrease connectivity between occipital and parietal areas due to disruptive visual input during sensory processing. We also hypothesized that physical pull perturbations would increase cortical connections to central sensorimotor areas because of higher sensorimotor integration demands. Our findings show decreased occipito-parietal connectivity during visual rotations and widespread increases in connectivity during pull perturbations focused on central areas, as expected. We also found evidence for communication from cortex to muscles during perturbed balance. These results show that sensorimotor perturbations to balance alter cortical networks and can be quantified using effective connectivity estimation.

EEG time-frequency analysis provides arguments for arm swing support in human gait control

BACKGROUND

Human gait benefits from arm swing, which requires four-limb co-ordination. The Supplementary Motor Area (SMA) is involved in multi-limb coordination. With its location anterior to the leg motor cortex and the pattern of its connections, this suggests a distinct role in gait control.

RESEARCH QUESTION

Is the SMA functionally implicated in gait-related arm swing?

METHODS

Ambulant electroencephalography (EEG) was employed during walking with and without arm swing in twenty healthy subjects (mean age: 64.9yrs, SD 7.2). Power changes across the EEG frequency spectrum were assessed by Event Related Spectral Perturbation (ERSP) analysis over both the putative SMA at electrode position Fz and additional sensorimotor regions.

RESULTS

During walking with arm swing, midline electrodes Fz and Cz showed a step-related pattern of Event Related Desynchronization (ERD) followed by Event Related Synchronization (ERS). Walking without arm swing was associated with significant ERD-ERS power reduction in the high-beta/low-gamma band over Fz and a power increase over Cz. Electrodes C3 and C4 revealed a pattern of ERD during contralateral- and ERS during ipsilateral leg swing. This ERD power decreased in gait without arm swing (low-frequency band). The ERSP pattern during walking with arm swing was similar at CP1 and CP2: ERD was seen during double support and the initial swing phase of the right leg, while a strong ERS emerged during the second half of the left leg's swing. Walking without arm swing showed a significant power reduction of this ERD-ERS pattern over CP2, while over CP1, ERS during left leg's swing turned into ERD.

CONCLUSION

The relation between arm swing in walking and a step-related ERD-ERS pattern in the high-beta/low-gamma band over the putative SMA, points at an SMA contribution to integrated cyclic anti-phase movements of upper- and lower limbs. This supports a cortical underpinning of arm swing support in gait control.

How are Muscle Synergies Affected by Electromyography Pre-Processing?

Muscle synergies have been used for decades to explain a variety of motor behaviors, both in humans and animals and, more recently, to steer rehabilitation strategies. However, many sources of variability such as factorization algorithms, criteria for dimensionality reduction and data pre-processing constitute a major obstacle to the successful comparison of the results obtained by different research groups. Starting from the canonical EMG processing we determined how variations in filter cut-off frequencies and normalization methods, commonly found in literature, affect synergy weights and inter-subject similarity (ISS) using experimental data related to a 15-muscles upper-limb reaching task. Synergy weights were not significantly altered by either normalization (maximum voluntary contraction - MVC - or maximum amplitude of the signal - SELF) or band-pass filter ([20-500 Hz] or [50-500] Hz). Normalization did, however, alter the amount of variance explained by a set of synergies, which is a criterion often used for model order selection. Comparing different low-pass (LP) filters (0.5 Hz, 4 Hz, 10 Hz, 20 Hz cut-offs) we showed that increasing the low pass filter cut-off had the effect of decreasing the variance accounted for by a set number of synergies and affected individual muscle contributions. Extreme smoothing (i.e., LP cut-off 0.5 Hz) enhanced the contrast between active and inactive muscles but had an unpredictable effect on the ISS. The results presented here constitute a further step towards a thoughtful EMG pre-processing for the extraction of muscle synergies.

Decoding of muscle activity from the sensorimotor cortex in freely behaving monkeys

Remarkable advances have recently been made in the development of Brain-Machine Interface (BMI) technologies for restoring or enhancing motor function. However, the application of these technologies may be limited to patients in static conditions, as these developments have been largely based on studies of animals (e.g., non-human primates) in constrained movement conditions. The ultimate goal of BMI technology is to enable individuals to move their bodies naturally or control external devices without physical constraints. Here, we demonstrate accurate decoding of muscle activity from electrocorticogram (ECoG) signals in unrestrained, freely behaving monkeys. We recorded ECoG signals from the sensorimotor cortex as well as electromyogram signals from multiple muscles in the upper arm while monkeys performed two types of movements with no physical restraints, as follows: forced forelimb movement (lever-pull task) and natural whole-body movement (free movement within the cage). As in previous reports using restrained monkeys, we confirmed that muscle activity during forced forelimb movement was accurately predicted from simultaneously recorded ECoG data. More importantly, we demonstrated that accurate prediction of muscle activity from ECoG data was possible in monkeys performing natural whole-body movement. We found that high-gamma activity in the primary motor cortex primarily contributed to the prediction of muscle activity during natural whole-body movement as well as forced forelimb movement. In contrast, the contribution of high-gamma activity in the premotor and primary somatosensory cortices was significantly larger during natural whole-body movement. Thus, activity in a larger area of the sensorimotor cortex was needed to predict muscle activity during natural whole-body movement. Furthermore, decoding models obtained from forced forelimb movement could not be generalized to natural whole-body movement, which suggests that decoders should be built individually and according to different behavior types. These results contribute to the future application of BMI systems in unrestrained individuals.

Configuration of electrical spinal cord stimulation through real-time processing of gait kinematics

Epidural electrical stimulation (EES) of the spinal cord and real-time processing of gait kinematics are powerful methods for the study of locomotion and the improvement of motor control after injury or in neurological disorders. Here, we describe equipment and surgical procedures that can be used to acquire chronic electromyographic (EMG) recordings from leg muscles and to implant targeted spinal cord stimulation systems that remain stable up to several months after implantation in rats and nonhuman primates. We also detail how to exploit these implants to configure electrical spinal cord stimulation policies that allow control over the degree of extension and flexion of each leg during locomotion. This protocol uses real-time processing of gait kinematics and locomotor performance, and can be configured within a few days. Once configured, stimulation bursts are delivered over specific spinal cord locations with precise timing that reproduces the natural spatiotemporal activation of motoneurons during locomotion. These protocols can also be easily adapted for the safe implantation of systems in the vicinity of the spinal cord and to conduct experiments involving real-time movement feedback and closed-loop controllers.

Cortical Correlates of Locomotor Muscle Synergy Activation in Humans: An Electroencephalographic Decoding Study

Muscular control during walking is believed to be simplified by the coactivation of muscles called muscle synergies. Although significant corticomuscular connectivity during walking has been reported, the level at which the cortical activity is involved in muscle activity (muscle synergy or individual muscle level) remains unclear. Here we examined cortical correlates of muscle activation during walking by brain decoding of activation of muscle synergies and individual muscles from electroencephalographic signals. We demonstrated that the activation of locomotor muscle synergies was decoded from slow cortical waves. In addition, the decoding accuracy for muscle synergies was greater than that for individual muscles and the decoding of individual muscle activation was based on muscle-synergy-related cortical information. These results indicate the cortical correlates of locomotor muscle synergy activation. These findings expand our understanding of the relationships between brain and locomotor muscle synergies and could accelerate the development of effective brain-machine interfaces for walking rehabilitation.

Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points

Despite the progress in understanding of neural codes, the studies of the cortico-muscular coupling still largely rely on interferential electromyographic (EMG) signal or its rectification for the assessment of motor neuron pool behavior. This assessment is non-trivial and should be used with precaution. Direct analysis of neural codes by decomposing the EMG, also known as neural decoding, is an alternative to EMG amplitude estimation. In this study, we propose a fully-deterministic hybrid surface EMG (sEMG) decomposition approach that combines the advantages of both template-based and Blind Source Separation (BSS) decomposition approaches, a.k.a. guided source separation (GSS), to identify motor unit (MU) firing patterns. We use the single-pass density-based clustering algorithm to identify possible cluster representatives in different sEMG channels. These cluster representatives are then used as initial points of modified gradient Convolution Kernel Compensation (gCKC) algorithm. Afterwards, we use the Kalman filter to reduce the noise impact and increase convergence rate of MU filter identification by gCKC. Moreover, we designed an adaptive soft-thresholding method to identify MU firing times out of estimated MU spike trains. We tested the proposed algorithm on a set of synthetic sEMG signals with known MU firing patterns. A grid of 9 × 10 monopolar surface electrodes with 5-mm inter-electrode distances in both directions was simulated. Muscle excitation was set to 10, 30, and 50%. Colored Gaussian zero-mean noise with the signal-to-noise ratio (SNR) of 10, 20, and 30 dB, respectively, was added to 16 s long sEMG signals that were sampled at 4,096 Hz. Overall, 45 simulated signals were analyzed. Our decomposition approach was compared with gCKC algorithm. Overall, in our algorithm, the average numbers of identified MUs and Rate-of-Agreement (RoA) were 16.41 ± 4.18 MUs and 84.00 ± 0.06%, respectively, whereas the gCKC identified 12.10 ± 2.32 MUs with the average RoA of 90.78 ± 0.08%. Therefore, the proposed GSS method identified more MUs than the gCKC, with comparable performance. Its performance was dependent on the signal quality but not the signal complexity at different force levels. The proposed algorithm is a promising new offline tool in clinical neurophysiology.

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.