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

Context-dependent reduction in corticomuscular coupling for balance control in chronic stroke survivors

Balance control is an important indicator of mobility and independence in activities of daily living. How the functional coupling between the cortex and the muscle for balance control is affected following stroke remains to be known. We investigated the changes in coupling between the cortex and leg muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Fourteen participants with stroke and ten healthy controls performed a challenging balance task. They stood on a computerized support surface that was either fixed (low difficulty condition) or sway-referenced with varying gain (medium and high difficulty conditions). We computed corticomuscular coherence between electrodes placed over the sensorimotor area (electroencephalography) and leg muscles (electromyography) and assessed balance performance using clinical and laboratory-based tests. We found significantly lower delta frequency band coherence in stroke participants when compared with healthy controls under medium difficulty condition, but not during low and high difficulty conditions. These differences were found for most of the distal but not for proximal leg muscle groups. No differences were found at other frequency bands. Participants with stroke showed poor balance clinical scores when compared with healthy controls, but no differences were found for laboratory-based tests. The observation of effects at distal but not at proximal muscle groups suggests differences in the (re)organization of the descending connections across two muscle groups for balance control. We argue that the observed group difference in delta band coherence indicates balance context-dependent alteration in mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support surface for balance maintenance following stroke.

Mobile neuroimaging: What we have learned about the neural control of human walking, with an emphasis on EEG-based research

Our understanding of the neural control of human walking has changed significantly over the last twenty years and mobile brain imaging methods have contributed substantially to current knowledge. High-density electroencephalography (EEG) has the advantages of being lightweight and mobile while providing temporal resolution of brain changes within a gait cycle. Advances in EEG hardware and processing methods have led to a proliferation of research on the neural control of locomotion in neurologically intact adults. We provide a narrative review of the advantages and disadvantages of different mobile brain imaging methods, then summarize findings from mobile EEG studies quantifying electrocortical activity during human walking. Contrary to historical views on the neural control of locomotion, recent studies highlight the widespread involvement of many areas, such as the anterior cingulate, posterior parietal, prefrontal, premotor, sensorimotor, supplementary motor, and occipital cortices, that show active fluctuations in electrical power during walking. The electrocortical activity changes with speed, stability, perturbations, and gait adaptation. We end with a discussion on the next steps in mobile EEG research.

Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study

Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, β, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and β bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, β ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods' efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

Dynamics of brain-muscle networks reveal effects of age and somatosensory function on gait

Walking is a complex motor activity that requires coordinated interactions between the sensory and motor systems. We used mobile EEG and EMG to investigate the brain-muscle networks involved in gait control during overground walking in young people, older people, and individuals with Parkinson's disease. Dynamic interactions between the sensorimotor cortices and eight leg muscles within a gait cycle were assessed using multivariate analysis. We identified three distinct brain-muscle networks during a gait cycle. These networks include a bilateral network, a left-lateralized network activated during the left swing phase, and a right-lateralized network active during the right swing. The trajectories of these networks are contracted in older adults, indicating a reduction in neuromuscular connectivity with age. Individuals with the impaired tactile sensitivity of the foot showed a selective enhancement of the bilateral network, possibly reflecting a compensation strategy to maintain gait stability. These findings provide a parsimonious description of interindividual differences in neuromuscular connectivity during gait.

Neural Correlates of Abnormal Cortical Gait Control in Parkinson's Disease: A Whole-Gait-Cycle EEG Study

OBJECTIVE

Electroencephalography (EEG) with high time-resolution allows for recording dynamic cortical activity during walking and provides new insight into the underlying pathophysiology of gait impairments in PD. However, traditional gait-phase-specific EEG analysis only measures the brain activities in the isolated gait phase, but neglects the between-gait-phase interactions as well as the whole-gait-cycle characteristics, and therefore is unable to effectively reflect the abnormal cortical gait control.

METHODS

In this study, we introduced three whole-gait-cycle measures of intra-stride EEG activity (i.e., mean desynchronization, amplitude of fluctuations, and coupling to the gait phase), and investigated their abnormalities in PD and relationships with gait impairments, which were further compared with the traditional gait-phase-specific measures.

RESULTS

Compared with healthy controls, PD patients showed overwhelming stronger desynchronizations (ERD) across the whole gait cycle in θ, α and low-β bands, implying a cortical compensatory strategy in response to the low efficiency of the motor network. Patients also exhibited weaker intra-stride ERD fluctuations in the central area in α and low-β bands, with reduced amplitude and less coupling to the gait phase, which were correlated with gait impairments in walking speed, gait rhythm and walking stability. However, gait-phase-specific EEG measures did not show any significant correlation with gait impairments in PD.

CONCLUSION

Our results demonstrated the efficiency of whole-gait-cycle EEG measures in characterizing the abnormal cortical gait control, and for the first time, associated gait impairments with weak intra-stride electrocortical fluctuations.

SIGNIFICANCE

The findings may shed light on the development of cortical-targeted interventions for PD.

EEG Dynamics of Locomotion and Balancing: Solution to Neuro-Rehabilitation

The past decade has witnessed tremendous growth in analyzing the cortical representation of human locomotion and balance using Electroencephalography (EEG). With the advanced developments in miniaturized electronics, wireless brain recording systems have been developed for mobile recordings, such as in locomotion. In this review, the cortical dynamics during locomotion are presented with extensive focus on motor imagery, and employing the treadmill as a tool for performing different locomotion tasks. Further, the studies that examine the cortical dynamics during balancing, focusing on two types of balancing tasks, ie, static and dynamic, with the challenges in sensory inputs and cognition (dual-task), are presented. Moreover, the current literature demonstrates the advancements in signal processing methods to detect and remove the artifacts from EEG signals. Prior studies show the electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex was found to be activated during locomotion. The event-related potential has been observed to increase in the fronto-central region for a wide range of balance tasks. The advanced knowledge of cortical dynamics during mobility can benefit various application areas such as neuroprosthetics and gait/balance rehabilitation. This review will be beneficial for the development of neuroprostheses, and rehabilitation devices for patients suffering from movement or neurological disorders.

Cortico-muscular connectivity is modulated by passive and active Lokomat-assisted Gait

The effects of robotic-assisted gait (RAG) training, besides conventional therapy, on neuroplasticity mechanisms and cortical integration in locomotion are still uncertain. To advance our knowledge on the matter, we determined the involvement of motor cortical areas in the control of muscle activity in healthy subjects, during RAG with Lokomat, both with maximal guidance force (100 GF-passive RAG) and without guidance force (0 GF-active RAG) as customary in rehabilitation treatments. We applied a novel cortico-muscular connectivity estimation procedure, based on Partial Directed Coherence, to jointly study source localized EEG and EMG activity during rest (standing) and active/passive RAG. We found greater cortico-cortical connectivity, with higher path length and tendency toward segregation during rest than in both RAG conditions, for all frequency bands except for delta. We also found higher cortico-muscular connectivity in distal muscles during swing (0 GF), and stance (100 GF), highlighting the importance of direct supraspinal control to maintain balance, even when gait is supported by a robotic exoskeleton. Source-localized connectivity shows that this control is driven mainly by the parietal and frontal lobes. The involvement of many cortical areas also in passive RAG (100 GF) justifies the use of the 100 GF RAG training for neurorehabilitation, with the aim of enhancing cortical-muscle connections and driving neural plasticity in neurological patients.

Regional activity and effective connectivity within the frontoparietal network during precision walking with visual cueing: an fNIRS study

Precision walking (PW) incorporates precise step adjustments into regular walking patterns to navigate challenging surroundings. However, the brain processes involved in PW control, which encompass cortical regions and interregional interactions, are not fully understood. This study aimed to investigate the changes in regional activity and effective connectivity within the frontoparietal network associated with PW. Functional near-infrared spectroscopy data were recorded from adult subjects during treadmill walking tasks, including normal walking (NOR) and PW with visual cues, wherein the intercue distance was either fixed (FIX) or randomly varied (VAR) across steps. The superior parietal lobule (SPL), dorsal premotor area (PMd), supplementary motor area (SMA), and dorsolateral prefrontal cortex (dlPFC) were specifically targeted. The results revealed higher activities in SMA and left PMd, as well as left-to-right SPL connectivity, in VAR than in FIX. Activities in SMA and right dlPFC, along with dlPFC-to-SPL connectivity, were higher in VAR than in NOR. Overall, these findings provide insights into the roles of different brain regions and connectivity patterns within the frontoparietal network in facilitating gait control during PW, providing a useful baseline for further investigations into brain networks involved in locomotion.

A Systematic Review of Gait Analysis in the Context of Multimodal Sensing Fusion and AI

BACKGROUND

Neurological diseases are a leading cause of disability and mortality. Gait, or human walking, is a significant predictor of quality of life, morbidity, and mortality. Gait patterns and other kinematic, kinetic, and balance gait features are accurate and powerful diagnostic and prognostic tools.

OBJECTIVE

This review article focuses on the applicability of gait analysis using fusion techniques and artificial intelligence (AI) models. The aim is to examine the significance of mixing several types of wearable and non-wearable sensor data and the impact of this combination on the performance of AI models.

METHOD

In this systematic review, 66 studies using more than two modalities to record and analyze gait were identified. 40 studies incorporated multiple gait analysis modalities without the use of artificial intelligence to extract gait features such as kinematic, kinetic, margin of stability, temporal, and spatial gait parameters, as well as cerebral activity. Similarly, 26 studies analyzed gait data using multimodal fusion sensors and AI algorithms.

RESULTS

The research summarized here demonstrates that the quality of gait analysis and the effectiveness of AI models can both benefit from the integration of data from many sensors. Meanwhile, the utilization of EMG signals in fusion data is especially advantageous.

CONCLUSION

The findings of this review suggest that a smart, portable, wearable-based gait and balance assessment system can be developed using multimodal sensing of the most cutting-edge, clinically relevant tools and technology available. The information presented in this article may serve as a vital springboard for such development.

Movement Artifact Suppression in Wearable Low-Density and Dry EEG Recordings Using Active Electrodes and Artifact Subspace Reconstruction

Wearable low-density dry electroencephalogram (EEG) headsets facilitate multidisciplinary applications of brain-activity decoding and brain-triggered interaction for healthy people in real-world scenarios. However, movement artifacts pose a great challenge to their validity in users with naturalistic behaviors (i.e., without highly controlled settings in a laboratory). High-precision, high-density EEG instruments commonly embed an active electrode infrastructure and/or incorporate an auxiliary artifact subspace reconstruction (ASR) pipeline to handle movement artifact interferences. Existing endeavors motivate this study to explore the efficacy of both hardware and software solutions in low-density and dry EEG recordings against non-tethered settings, which are rarely found in the literature. Therefore, this study employed a LEGO-like electrode-holder assembly grid to coordinate three 3-channel system designs (with passive/active dry vs. passive wet electrodes). It also conducted a simultaneous EEG recording while performing an oddball task during treadmill walking, with speeds of 1 and 2 KPH. The quantitative metrics of pre-stimulus noise, signal-to-noise ratio, and inter-subject correlation from the collected event-related potentials of 18 subjects were assessed. Results indicate that while treating a passive-wet system as benchmark, only the active-electrode design more or less rectified movement artifacts for dry electrodes, whereas the ASR pipeline was substantially compromised by limited electrodes. These findings suggest that a lightweight, minimally obtrusive dry EEG headset should at least equip an active-electrode infrastructure to withstand realistic movement artifacts for potentially sustaining its validity and applicability in real-world scenarios.

Task-dependent Alteration in Delta Band Corticomuscular Coherence during Standing in Chronic Stroke Survivors

Balance control is an important indicator of mobility and independence in activities of daily living. How the changes in functional integrity of corticospinal tract due to stroke affects the maintenance of upright stance remains to be known. We investigated the changes in functional coupling between the cortex and lower limb muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Eleven stroke patients and nine healthy controls performed a challenging balance task. They stood on a computerized platform with/without somatosensory input distortion created by sway-referencing the support surface, thereby varying the difficulty levels of the task. We computed corticomuscular coherence between Cz (electroencephalography) and leg muscles and assessed balance performance using Berg Balance scale (BBS), Timed-up and go (TUG) and center of pressure (COP) measures. We found lower delta frequency band coherence in stroke patients when compared with healthy controls under medium difficulty condition for distal but not proximal leg muscles. For both groups, we found similar coherence at other frequency bands. On BBS and TUG, stroke patients showed poor balance. However, similar group differences were not consistently observed across COP measures. The presence of distal versus proximal effect suggests differences in the (re)organization of the corticospinal connections across the two muscles groups for balance control. We argue that the observed group difference in the delta coherence might be due to altered mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support platform for balance control.

Microsynt: exploring the syntax of EEG microstates

Microstates represent electroencephalographic (EEG) activity as a sequence of switching, transient, metastable states. Growing evidence suggests the useful information on brain states is to be found in the higher-order temporal structure of these sequences. Instead of focusing on transition probabilities, here we propose "Microsynt", a method designed to highlight higher-order interactions that form a preliminary step towards understanding the syntax of microstate sequences of any length and complexity. Microsynt extracts an optimal vocabulary of "words" based on the length and complexity of the full sequence of microstates. Words are then sorted into classes of entropy and their representativeness within each class is statistically compared with surrogate and theoretical vocabularies. We applied the method on EEG data previously collected from healthy subjects undergoing propofol anaesthesia, and compared their "fully awake" (BASE) and "fully unconscious" (DEEP) conditions. Results show that microstate sequences, even at rest, are not random but tend to behave in a more predictable way, favoring simpler sub-sequences, or "words". Contrary to high-entropy words, lowest-entropy binary microstate loops are prominent and favored on average 10 times more than what is theoretically expected. Progressing from BASE to DEEP, the representation of low-entropy words increases while that of high-entropy words decreases. During the awake state, sequences of microstates tend to be attracted towards "A - B - C" microstate hubs, and most prominently A - B binary loops. Conversely, with full unconsciousness, sequences of microstates are attracted towards "C - D - E" hubs, and most prominently C - E binary loops, confirming the putative relation of microstates A and B to externally-oriented cognitive processes and microstate C and E to internally-generated mental activity. Microsynt can form a syntactic signature of microstate sequences that can be used to reliably differentiate two or more conditions.

Wireless EEG: A survey of systems and studies

The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturization. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 48 wireless EEG devices along with a number of important-sometimes difficult-to-obtain-features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorized by application and analyzed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary about pitfalls and caveats regarding this increasingly accessible line of research.

Distinct cortico-muscular coupling between step and stance leg during reactive stepping responses

Balance recovery often relies on successful stepping responses, which presumably require precise and rapid interactions between the cerebral cortex and the leg muscles. Yet, little is known about how cortico-muscular coupling (CMC) supports the execution of reactive stepping. We conducted an exploratory analysis investigating time-dependent CMC with specific leg muscles in a reactive stepping task. We analyzed high density EEG, EMG, and kinematics of 18 healthy young participants while exposing them to balance perturbations at different intensities, in the forward and backward directions. Participants were instructed to maintain their feet in place, unless stepping was unavoidable. Muscle-specific Granger causality analysis was conducted on single step- and stance-leg muscles over 13 EEG electrodes with a midfrontal scalp distribution. Time-frequency Granger causality analysis was used to identify CMC from cortex to muscles around perturbation onset, foot-off and foot strike events. We hypothesized that CMC would increase compared to baseline. In addition, we expected to observe different CMC between step and stance leg because of their functional role during the step response. In particular, we expected that CMC would be most evident for the agonist muscles while stepping, and that CMC would precede upregulation in EMG activity in these muscles. We observed distinct Granger gain dynamics over theta, alpha, beta, and low/high-gamma frequencies during the reactive balance response for all leg muscles in each step direction. Interestingly, between-leg differences in Granger gain were almost exclusively observed following the divergence of EMG activity. Our results demonstrate cortical involvement in the reactive balance response and provide insights into its temporal and spectral characteristics. Overall, our findings suggest that higher levels of CMC do not facilitate leg-specific EMG activity. Our work is relevant for clinical populations with impaired balance control, where CMC analysis may elucidate the underlying pathophysiological mechanisms.

Case report: A novel approach of closed-loop brain stimulation combined with robot gait training in post-stroke gait disturbance

Most post-stroke patients have long-lasting gait disturbances that reduce their daily activities. They often show impaired hip and knee joint flexion and ankle dorsiflexion of the lower limbs during the swing phase of gait, which is controlled by the corticospinal tract from the primary motor cortex (M1). Recently, we reported that gait-synchronized closed-loop brain stimulation targeting swing phase-related activity in the affected M1 can improve gait function in post-stroke patients. Subsequently, a gait-training robot (Orthobot^®) was developed that could assist lower-limb joint movements during the swing phase of gait. Therefore, we investigated whether gait-synchronized closed-loop brain stimulation combined with robot-assisted training targeting the swing phase could enhance the recovery of post-stroke gait disturbance. A 57-year-old female patient with chronic post-stroke hemiparesis underwent closed-loop brain stimulation combined with robot-assisted training for 10 min 2 years after left pons infarction. For closed-loop brain stimulation, we used transcranial oscillatory electrical current stimulation over the lesioned M1 foot area with 1.5 mA of DC offset and 0-3 mA of sine-wave formed currents triggered by the paretic heel contact to set the maximum current just before the swing phase (intervention A; two times repeated, A1 and A2). According to the N-of-1 study design, we also performed sham stimulation (intervention B) and control stimulation not targeting the swing phase (intervention C) combined with robot-assisted training in the order of A1-B-A2-C interventions. As a result, we found larger improvements in gait speed, the Timed Up and Go test result, and muscle strength after the A1 and A2 interventions than after the B and C interventions. After confirming the short-term effects, we performed an additional long-term intervention twice a week for 5 weeks, for a total of 10 sessions. Gait parameters also largely improved after long-term intervention. Gait-synchronized closed-loop brain stimulation combined with robot-assisted training targeting the swing phase of gait may promote the recovery of gait function in post-stroke patients. Further studies with a larger number of patients are necessary.

Relationship between lower limb muscle activity and cortical activation among elderly people during walking: Effects of fast speed and cognitive dual task

Objective

Gait is a complex behavior that involves not only the musculoskeletal system, but also higher-order brain functions, including cognition. This study was performed to investigate the correlation between lower limb muscle activity and cortical activation during treadmill walking in two groups of elderly people: the young-old (aged 65-74 years) and the old-old (aged 75-84 years).

Methods

Thirty-one young-old and 31 old-old people participated in this study. All participants were sequentially subjected to three gait conditions on a treadmill: (1) comfortable walking, (2) fast walking, and (3) cognitive dual-task walking. During treadmill walking, the activity of the lower limb muscles was measured using a surface electromyography system, and cortical activation was measured using a functional near-infrared spectroscopy system. The correlation between muscle activity and cortical activation during treadmill walking was analyzed and compared between the two groups.

Results

During comfortable walking, lower extremity muscle activity had a strong correlation with cortical activation, especially in the swing phase; this was significantly stronger in the young-old than the old-old. During fast walking, the correlations between lower limb muscle activity and cortical activation were stronger than those during comfortable walking in both groups. In cognitive dual-task walking, cortical activation in the frontal region and motor area was increased, although the correlation between muscle activity and cortical activation was weaker than that during comfortable walking in both groups.

Conclusion

The corticomotor correlation differed significantly between the old-old and the young-old. These results suggest that gait function is compensated by regulating corticomotor correlation as well as brain activity during walking in the elderly. These results could serve as a basis for developing gait training and fall prevention programs for the elderly.

Experiment protocols for brain-body imaging of locomotion: A systematic review

Introduction

Human locomotion is affected by several factors, such as growth and aging, health conditions, and physical activity levels for maintaining overall health and well-being. Notably, impaired locomotion is a prevalent cause of disability, significantly impacting the quality of life of individuals. The uniqueness and high prevalence of human locomotion have led to a surge of research to develop experimental protocols for studying the brain substrates, muscle responses, and motion signatures associated with locomotion. However, from a technical perspective, reproducing locomotion experiments has been challenging due to the lack of standardized protocols and benchmarking tools, which impairs the evaluation of research quality and the validation of previous findings.

Methods

This paper addresses the challenges by conducting a systematic review of existing neuroimaging studies on human locomotion, focusing on the settings of experimental protocols, such as locomotion intensity, duration, distance, adopted brain imaging technologies, and corresponding brain activation patterns. Also, this study provides practical recommendations for future experiment protocols.

Results

The findings indicate that EEG is the preferred neuroimaging sensor for detecting brain activity patterns, compared to fMRI, fNIRS, and PET. Walking is the most studied human locomotion task, likely due to its fundamental nature and status as a reference task. In contrast, running has received little attention in research. Additionally, cycling on an ergometer at a speed of 60 rpm using fNIRS has provided some research basis. Dual-task walking tasks are typically used to observe changes in cognitive function. Moreover, research on locomotion has primarily focused on healthy individuals, as this is the scenario most closely resembling free-living activity in real-world environments.

Discussion

Finally, the paper outlines the standards and recommendations for setting up future experiment protocols based on the review findings. It discusses the impact of neurological and musculoskeletal factors, as well as the cognitive and locomotive demands, on the experiment design. It also considers the limitations imposed by the sensing techniques used, including the acceptable level of motion artifacts in brain-body imaging experiments and the effects of spatial and temporal resolutions on brain sensor performance. Additionally, various experiment protocol constraints that need to be addressed and analyzed are explained.

Feature stability and setup minimization for EEG-EMG-enabled monitoring systems

Delivering health care at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movement recognition and monitoring of the associated brain signals. This is one of the contexts where it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for the classification of a specific type of hand movement. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a mapping-and-aggregation strategy to increase the interpretability of the machine learning results. The proposed approach provides very low mis-classification errors ( < 0.1 ), with very few and stable MSC features ( < 10 % of the initial set of available features). Furthermore, we identified a common pattern across algorithms and classification problems, i.e., the activation of the centro-parietal brain areas and arm's muscles in 8-80 Hz frequency band, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable gesture recognition.

Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait

Walking on natural terrain while performing a dual-task, such as typing on a smartphone is a common behavior. Since dual-tasking and terrain change gait characteristics, it is of interest to understand how altered gait is reflected by changes in gait-associated neural signatures. A study was performed with 64-channel electroencephalography (EEG) of healthy volunteers, which was recorded while they walked over uneven and even terrain outdoors with and without performing a concurrent task (self-paced button pressing with both thumbs). Data from n = 19 participants (M = 24 years, 13 females) were analyzed regarding gait-phase related power modulations (GPM) and gait performance (stride time and stride time-variability). GPMs changed significantly with terrain, but not with the task. Descriptively, a greater beta power decrease following right-heel strikes was observed on uneven compared to even terrain. No evidence of an interaction was observed. Beta band power reduction following the initial contact of the right foot was more pronounced on uneven than on even terrain. Stride times were longer on uneven compared to even terrain and during dual- compared to single-task gait, but no significant interaction was observed. Stride time variability increased on uneven terrain compared to even terrain but not during single- compared to dual-tasking. The results reflect that as the terrain difficulty increases, the strides become slower and more irregular, whereas a secondary task slows stride duration only. Mobile EEG captures GPM differences linked to terrain changes, suggesting that the altered gait control demands and associated cortical processes can be identified. This and further studies may help to lay the foundation for protocols assessing the cognitive demand of natural gait on the motor system.

Learning neural decoders without labels using multiple data streams

OBJECTIVE

Recent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. Alternatively, self-supervised models that share self-generated pseudo-labels between two data streams have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding.

APPROACH

We learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to train decoders that can classify movements from brain recordings. After training, we then isolate the decoders for each input data stream and compare the accuracy of decoders trained using cross-modal deep clustering against supervised and unimodal, self-supervised models.

MAIN RESULTS

We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we extend cross-modal decoder training to three or more modalities, achieving state-of-the-art neural decoding accuracy that matches or slightly exceeds the performance of supervised models. Significance: We demonstrate that cross-modal, self-supervised decoding can be applied to train neural decoders when few or no labels are available and extend the cross-modal framework to share information among three or more data streams, further improving self-supervised training.

Directed network analysis reveals changes in cortical and muscular connectivity caused by different standing balance tasks

Objective.Standing balance forms the basis of daily activities that require the integration of multi-sensory information and coordination of multi-muscle activation. Previous studies have confirmed that the cortex is directly involved in balance control, but little is known about the neural mechanisms of cortical integration and muscle coordination in maintaining standing balance.Approach.We used a direct directed transfer function (dDTF) to analyze the changes in the cortex and muscle connections of healthy subjects (15 subjects: 13 male and 2 female) corresponding to different standing balance tasks.Main results.The results show that the topology of the EEG brain network and muscle network changes significantly as the difficulty of the balancing tasks increases. For muscle networks, the connection analysis shows that the connection of antagonistic muscle pairs plays a major role in the task. For EEG brain networks, graph theory-based analysis shows that the clustering coefficient increases significantly, and the characteristic path length decreases significantly with increasing task difficulty. We also found that cortex-to-muscle connections increased with the difficulty of the task and were significantly stronger than the muscle-to-cortex connections.Significance.These results show that changes in the difficulty of balancing tasks alter EEG brain networks and muscle networks, and an analysis based on the directed network can provide rich information for exploring the neural mechanisms of balance control.

Assessing Neurokinematic and Neuromuscular Connectivity During Walking Using Mobile Brain-Body Imaging

Gait is a common but rather complex activity that supports mobility in daily life. It requires indeed sophisticated coordination of lower and upper limbs, controlled by the nervous system. The relationship between limb kinematics and muscular activity with neural activity, referred to as neurokinematic and neuromuscular connectivity (NKC/NMC) respectively, still needs to be elucidated. Recently developed analysis techniques for mobile high-density electroencephalography (hdEEG) recordings have enabled investigations of gait-related neural modulations at the brain level. To shed light on gait-related neurokinematic and neuromuscular connectivity patterns in the brain, we performed a mobile brain/body imaging (MoBI) study in young healthy participants. In each participant, we collected hdEEG signals and limb velocity/electromyography signals during treadmill walking. We reconstructed neural signals in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–50 Hz) frequency bands, and assessed the co-modulations of their power envelopes with myogenic/velocity envelopes. Our results showed that the myogenic signals have larger discriminative power in evaluating gait-related brain-body connectivity with respect to kinematic signals. A detailed analysis of neuromuscular connectivity patterns in the brain revealed robust responses in the alpha and beta bands over the lower limb representation in the primary sensorimotor cortex. There responses were largely contralateral with respect to the body sensor used for the analysis. By using a voxel-wise analysis of variance on the NMC images, we revealed clear modulations across body sensors; the variability across frequency bands was relatively lower, and below significance. Overall, our study demonstrates that a MoBI platform based on hdEEG can be used for the investigation of gait-related brain-body connectivity. Future studies might involve more complex walking conditions to gain a better understanding of fundamental neural processes associated with gait control, or might be conducted in individuals with neuromotor disorders to identify neural markers of abnormal gait.

Frequency-dependent modulation of neural oscillations across the gait cycle

Balance and walking are fundamental to support common daily activities. Relatively accurate characterizations of normal and impaired gait features were attained at the kinematic and muscular levels. Conversely, the neural processes underlying gait dynamics still need to be elucidated. To shed light on gait‐related modulations of neural activity, we collected high‐density electroencephalography (hdEEG) signals and ankle acceleration data in young healthy participants during treadmill walking. We used the ankle acceleration data to segment each gait cycle in four phases: initial double support, right leg swing, final double support, left leg swing. Then, we processed hdEEG signals to extract neural oscillations in alpha, beta, and gamma bands, and examined event‐related desynchronization/synchronization (ERD/ERS) across gait phases. Our results showed that ERD/ERS modulations for alpha, beta, and gamma bands were strongest in the primary sensorimotor cortex (M1), but were also found in premotor cortex, thalamus and cerebellum. We observed a modulation of neural oscillations across gait phases in M1 and cerebellum, and an interaction between frequency band and gait phase in premotor cortex and thalamus. Furthermore, an ERD/ERS lateralization effect was present in M1 for the alpha and beta bands, and in the cerebellum for the beta and gamma bands. Overall, our findings demonstrate that an electrophysiological source imaging approach based on hdEEG can be used to investigate dynamic neural processes of gait control. Future work on the development of mobile hdEEG‐based brain–body imaging platforms may enable overground walking investigations, with potential applications in the study of gait disorders.

EEG microstate dynamics indicate a U-shaped path to propofol-induced loss of consciousness

Evidence suggests that the stream of consciousness is parsed into transient brain states manifesting themselves as discrete spatiotemporal patterns of global neuronal activity. Electroencephalographical (EEG) microstates are proposed as the neurophysiological correlates of these transiently stable brain states that last for fractions of seconds. To further understand the link between EEG microstate dynamics and consciousness, we continuously recorded high-density EEG in 23 surgical patients from their awake state to unconsciousness, induced by step-wise increasing concentrations of the intravenous anesthetic propofol. Besides the conventional parameters of microstate dynamics, we introduce a new implementation of a method to estimate the complexity of microstate sequences. The brain activity under the surgical anesthesia showed a decreased sequence complexity of the stereotypical microstates, which became sparser and longer-lasting. However, we observed an initial increase in microstates' temporal dynamics and complexity with increasing depth of sedation leading to a distinctive "U-shape" that may be linked to the paradoxical excitation induced by moderate levels of propofol. Our results support the idea that the brain is in a metastable state under normal conditions, balancing between order and chaos in order to flexibly switch from one state to another. The temporal dynamics of EEG microstates indicate changes of this critical balance between stability and transition that lead to altered states of consciousness.

Effects of Transcranial Direct Current Stimulation of Bilateral Supplementary Motor Area on the Lower Limb Motor Function in a Stroke Patient with Severe Motor Paralysis: A Case Study

In patients with severe motor paralysis, increasing the excitability of the supplementary motor area (SMA) in the non-injured hemisphere contributes to the recovery of lower limb motor function. However, the contribution of transcranial direct current stimulation (tDCS) over the SMA of the non-injured hemisphere in the recovery of lower limb motor function is unclear. This study aimed to examine the effects of tDCS on bilateral hemispheric SMA combined with assisted gait training. A post-stroke patient with severe motor paralysis participated in a retrospective AB design. Assisted gait training was performed only in period A and tDCS to the SMA of the bilateral hemisphere combined with assisted gait training (bi-tDCS) was performed in period B. Additionally, three conditions were performed for 20 min each in the intervals between the two periods: (1) assisted gait training only, (2) assisted gait training combined with tDCS to the SMA of the injured hemisphere, and (3) bi-tDCS. Measurements were muscle activity and beta-band intermuscular coherence (reflecting corticospinal tract excitability) of the vastus medialis muscle. The bi-tDCS immediately and longitudinally increased muscle activity and intermuscular coherence. We consider that bi-tDCS may be effective in recovering lower limb motor function in a patient with severe motor paralysis.

The role of temporal distance of the events on the spatiotemporal dynamics of mental time travel to one's personal past and future

Mental time travel to personal past and future events shows remarkable cognitive and neural similarities. Both temporalities seem to rely on the same core network involving episodic binding and monitoring processes. However, it is still unclear in what way the temporal distance of the simulated events modulates the recruitment of this network when mental time-travelling to the past and the future. The present study explored the electrophysiological correlates of remembering and imagining personal events at two temporal distances from the present moment (near and far). Temporal distance modulated the late parietal component (LPC) and the late frontal effect (LFE), respectively involved in episodic and monitoring processes. Interestingly, temporal distance modulations differed in the past and future event simulation, suggesting greater episodic processing for near as opposed to far future situations (with no differences on near and far past), and the implementation of greater post-simulation monitoring processes for near past as compared to far past events (with high demands on both near and far future). These findings show that both past and future event simulations are affected by the temporal distance of the events, although not exactly in a mirrored way. They are discussed according to the increasing role of semantic memory in episodic mental time travel to farther temporal distances from the present.

Merged swing-muscle synergies and their relation to walking characteristics in subacute post-stroke patients: An observational study

In post-stroke patients, muscle synergy (the coordination of motor modules during walking) is impaired. In some patients, the muscle synergy termed module 1 (hip/knee extensors) is merged with module 2 (ankle plantar flexors), and in other cases, module 1 is merged with module 4 (knee flexors). However, post-stroke individuals with a merging pattern of module 3 (hip flexor and ankle dorsiflexor) and module 4, which is the swing-muscle synergy, have not been reported. This study aimed to determine the muscle-synergy merging subtypes of post-stroke during comfortable walking speed (cws). We also examined the effect of experimental lower-limb angle modulation on the muscle synergy patterns of walking in each subtype. Forty-one participants were assessed under three conditions: cws, long stepping on the paretic side (p-long), and long stepping on the non-paretic side (np-long). Lower-limb flexion and extension angles and the electromyogram were measured during walking. Subtype classification was based on the merging pattern of the muscle synergies, and we examined the effect of different lower-limb angles on the muscle synergies. We identified three merging subtypes: module 1 with module 2 (subtype 1), module 1 with module 4 (subtype 2), and module 3 with module 4 (subtype 3). In the cws condition, the lower-limb flexion angle was reduced in subtype 3, and the lower-limb extension angle was decreased in subtype 1. A more complex muscle synergy was observed only in subtype 3 in the p-long condition versus cws (p = 0.036). This subtype classification of walking impairments based on the merging pattern of the muscle synergies could be useful for the selection of a rehabilitation strategy according to the individual’s particular neurological condition. Rehabilitation with increased lower-limb flexion may be effective for the training of patients with merging of modules 3 and 4 in comfortable walking.

A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data

Objective.Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which-depending on their extent-may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts.Approach.In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches.Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts.Significance.Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.

Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running

We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.

The strength of corticomotoneuronal drive underlies ALS split phenotypes and reflects early upper motor neuron dysfunction

BACKGROUND

Split phenotypes, (split hand, elbow, leg, and foot), are probably unique to ALS, and are characterized by having a shared peripheral input of both affected and unaffected muscles. This implies an anatomical origin rostral to the spinal cord, primarily within the cerebral cortex. Therefore, split phenotypes are a potential marker of ALS upper motor neuron pathology. However, to date, reports documenting upper motor neuron dysfunction in split phenotypes have been limited to using transcranial magnetic stimulation and cortical threshold tracking techniques. Here, we consider several other potential methodologies that could confirm a primary upper motor neuron pathology in split phenotypes.

METHODS

We review the potential of: 1. measuring the compound excitatory post-synaptic potential recorded from a single activated motor unit, 2. cortical-muscular coherence, and 3. new advanced modalities of neuroimaging (high-resolution imaging protocols, ultra-high field MRI platforms [7T], and novel Non-Gaussian diffusion models).

CONCLUSIONS

We propose that muscles involved in split phenotypes are those functionally involved in the human motor repertoire used particularly in complex activities. Their anterior horn cells receive the strongest corticomotoneuronal input. This is also true of the weakest muscles that are the earliest to be affected in ALS. Descriptions of split hand in non-ALS cases and proposals that peripheral nerve or muscle dysfunction may be causative are contentious. Only a few carefully controlled cases of each form of split phenotype, using upper motor neuron directed methodologies, are necessary to prove our postulate.

The Effects of Paired Associative Stimulation with Transcutaneous Spinal Cord Stimulation on Corticospinal Excitability in Multiple Lower-limb Muscles

Paired associative stimulation (PAS) is a non-invasive method to modulate the excitability of the primary motor cortex (M1). PAS involves the combination of peripheral nerve stimulation and transcranial magnetic stimulation (TMS) over the primary motor cortex. However, for lower-limb muscles, PAS has only been applied to the few muscles innervated by peripheral nerves that can easily be stimulated. This study used transcutaneous spinal cord stimulation (tSCS) to the posterior root, stimulating the sensory nerves of multiple lower-limb muscles, and aimed to investigate the effect of PAS consisting of tSCS and TMS on corticospinal excitability. Twelve non-disabled men received 120 paired stimuli on two separate days in (1) an individual-ISI condition, using inter-stimulus intervals (ISIs) of paired stimuli individually calculated to send two signals to M1 with individually-adjusted ISI, and (2) a constant-ISI condition, using a constant ISI of 100 ms. Before and after PAS, corticospinal excitability was assessed in the lower-limb muscles. Facilitation of corticospinal excitability in the lower-leg and hamstring muscles was observed up to 30 min after PAS only in the individual-ISI condition (p < 0.05), although there was no significant difference between the individual-ISI and constant-ISI conditions. Additionally, our results revealed a difference in PAS-induced facilitation among lower-limb muscles, suggesting a spatial gradient of PAS-induced facilitation of corticospinal excitability, such that knee flexor muscles have a higher potential for plastic change than knee extensor muscles. These findings will foster a better understanding of the neural mechanisms underlying PAS-induced neuroplasticity, leading to better neurorehabilitation and motor learning strategies.

Preferred Limb Reaction, Swing and Recovery Step Times between Subjects with and without Chronic Low Back Pain

A compensatory stepping strategy following repeated perturbations may compromise dynamic balance and postural stability. However, there is a lack of study on preferred limb reaction, swing, and step time adjustments. The purpose of this study was to investigate limb reaction, swing, and recovery step times following repeated trip perturbations in individuals with and without non-specific chronic low back pain (LBP). There were 30 subjects with LBP and 50 control subjects who participated in the study. The limb reaction, swing, and recovery step times (s) were measured following treadmill-induced random repeated perturbations (0.12 m/s velocity for 62.5 cm displacement), which caused subjects to move forward for 4.90 s. Both groups demonstrated a significant interaction of repetitions and times (F = 4.39, p = 0.03). Specifically, the recovery step time was significantly shorter in the LBP group during the first trip (t = 2.23, p = 0.03). There was a significant interaction on repetitions and times (F = 6.03, p = 0.02) in the LBP group, and the times were significantly different (F = 45.04, p = 0.001). The initial limb reaction time of the LBP group was significantly correlated with three repeated swing times to avoid falls. The novelty of the first trip tends to enhance a protective strategy implemented by the LBP group. Although limb preference did not demonstrate a significant difference between groups, the LBP group demonstrated shorter recovery step times on their preferred limb initially in order to implement an adaptive strategy to avoid fall injuries following repeated perturbations.

Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations

In this article, we evaluated the variations of the brain and muscle activations while subjects are exposed to different perturbations to walking and standing balance. Since EEG and EMG signals have complex structures, we utilized the complexity-based analysis. Specifically, we analyzed the fractal dimension and sample entropy of Electroencephalogram (EEG) and Electromyogram (EMG) signals while subjects walked and stood, and received different perturbations in the form of pulling and rotation (via virtual reality). The results showed that the complexity of EEG signals was higher in walking than standing as the result of different perturbations. However, the complexity of EMG signals was higher in standing than walking as the result of different perturbations. Therefore, the alterations in the complexity of EEG and EMG signals are inversely correlated. This analysis could be extended to investigate simultaneous variations of rhythmic patterns of other physiological signals while subjects perform different activities.

Disrupted cortico-peripheral interactions in motor disorders

Motor disorders may arise from neurological damage or diseases at different levels of the hierarchical motor control system and side-loops. Altered cortico-peripheral interactions might be essential characteristics indicating motor dysfunctions. By integrating cortical and peripheral responses, top-down and bottom-up cortico-peripheral coupling measures could provide new insights into the motor control and recovery process. This review first discusses the neural bases of cortico-peripheral interactions, and corticomuscular coupling and corticokinematic coupling measures are addressed. Subsequently, methodological efforts are summarized to enhance the modeling reliability of neural coupling measures, both linear and nonlinear approaches are introduced. The latest progress, limitations, and future directions are discussed. Finally, we emphasize clinical applications of cortico-peripheral interactions in different motor disorders, including stroke, neurodegenerative diseases, tremor, and other motor-related disorders. The modified interaction patterns and potential changes following rehabilitation interventions are illustrated. Altered coupling strength, modified coupling directionality, and reorganized cortico-peripheral activation patterns are pivotal attributes after motor dysfunction. More robust coupling estimation methodologies and combination with other neurophysiological modalities might more efficiently shed light on motor control and recovery mechanisms. Future studies with large sample sizes might be necessary to determine the reliabilities of cortico-peripheral interaction measures in clinical practice.

Stimulus evoked causality estimation in stereo-EEG

Objective.Stereo-electroencephalography (SEEG) has recently gained importance in analyzing brain functions. Its high temporal resolution and spatial specificity make it a powerful tool to investigate the strength, direction, and spectral content of brain networks interactions, especially when these connections are stimulus-evoked. However, choosing the best approach to evaluate the flow of information is not trivial, due to the lack of validated methods explicitly designed for SEEG.Approach.We propose a novel non-parametric statistical test for event-related causality (ERC) assessment on SEEG recordings. Here, we refer to the ERC as the causality evoked by a particular part of the stimulus (a response window (RW)). We also present a data surrogation method to evaluate the performance of a causality estimation algorithm. We finally validated our pipeline using surrogate SEEG data derived from an experimentally collected dataset, and compared the most used and successful measures to estimate effective connectivity, belonging to the Geweke-Granger causality framework.Main results.Here we show that our workflow correctly identified all the directed connections in the RW of the surrogate data and prove the robustness of the procedure against synthetic noise with amplitude exceeding physiological-plausible values. Among the causality measures tested, partial directed coherence performed best.Significance.This is the first non-parametric statistical test for ERC estimation explicitly designed for SEEG datasets. The pipeline, in principle, can also be applied to the analysis of any type of time-varying estimator, if there exists a clearly defined RW.

Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling

As a common neurophysiological phenomenon, voluntary muscle fatigue is accompanied by changes in both the central nervous system and peripheral muscles. Considering the effectiveness of the muscle network and the functional corticomuscular coupling (FCMC) in analyzing motor function, muscle fatigue can be analyzed by quantitating the intermuscular coupling and corticomuscular coupling. However, existing coherence-based research on muscle fatigue are limited by the inability of the coherence algorithm to identify the coupling direction, which cannot further reveal the underlying neural mechanism of muscle fatigue. To address this problem, we applied the time-delayed maximal information coefficient (TDMIC) method to quantitate the directional informational interaction in the muscle network and FCMC during a right-hand stabilized grip task. Eight healthy subjects were recruited to the present study. For the muscle networks, the beta-band information flow increased significantly due to muscle fatigue, and the information flow between the synergist muscles were stronger than that between the synergist and antagonist muscles. The information flow in the muscle network mainly flows to flexor digitorum superficialis (FDS), flexor carpi ulnar (FCU), and brachioradialis (BR). For the FCMC, muscle fatigue caused a significant decrease in the beta- and gamma-band bidirectional information flow. Further analysis revealed that the beta-band information flow was significantly stronger in the descending direction [electroencephalogram (EEG) to surface electromyography (sEMG)] than that in the ascending direction (sEMG to EEG) during pre-fatigue tasks. After muscle fatigue, the beta-band information flow in the ascending direction was significantly stronger than that in the descending direction. The present study demonstrates the influence of muscle fatigue on information flow in muscle networks and FCMC. We proposes that beta-band intermuscular and corticomuscular informational interaction plays an adjusting role in autonomous movement completion under muscle fatigue. Directed information flow analysis can be used as an effective method to explore the neural mechanism of muscle fatigue on the macroscopic scale.

Pathway-specific cortico-muscular coherence in proximal-to-distal compensation during fine motor control of finger extension after stroke

Objective.Proximal-to-distal compensation is commonly observed in the upper extremity (UE) after a stroke, mainly due to the impaired fine motor control in hand joints. However, little is known about its related neural reorganization. This study investigated the pathway-specific corticomuscular interaction in proximal-to-distal UE compensation during fine motor control of finger extension post-stroke by directed corticomuscular coherence (dCMC).Approach.We recruited 14 chronic stroke participants and 11 unimpaired controls. Electroencephalogram (EEG) from the sensorimotor area was concurrently recorded with electromyography (EMG) from extensor digitorum (ED), flexor digitorum (FD), triceps brachii (TRI) and biceps brachii (BIC) muscles in both sides of the stroke participants and in the dominant (right) side of the controls during the unilateral isometric finger extension at 20% maximal voluntary contractions. The dCMC was analyzed in descending (EEG → EMG) and ascending pathways (EMG → EEG) via the directed coherence. It was also analyzed in stable (segments with higher EMG stability) and less-stable periods (segments with lower EMG stability) subdivided from the whole movement period to investigate the fine motor control. Finally, the corticomuscular conduction time was estimated by dCMC phase delay.Main results.The affected limb had significantly lower descending dCMC in distal UE (ED and FD) than BIC (P< 0.05). It showed the descending dominance (significantly higher descending dCMC than the ascending,P< 0.05) in proximal UE (BIC and TRI) rather than the distal UE as in the controls. In the less-stable period, the affected limb had significantly lower EMG stability but higher ascending dCMC (P< 0.05) in distal UE than the controls. Furthermore, significantly prolonged descending conduction time (∼38.8 ms) was found in ED in the affected limb than the unaffected (∼26.94 ms) and control limbs (∼25.74 ms) (P< 0.05).Significance.The proximal-to-distal UE compensation in fine motor control post-stroke exhibited altered descending dominance from the distal to proximal UE, increased ascending feedbacks from the distal UE for fine motor control, and prolonged descending conduction time in the agonist muscle.

Amble Gait EEG Points at Complementary Cortical Networks Underlying Stereotypic Multi-Limb Co-ordination

Background

Walking is characterized by stable antiphase relations between upper and lower limb movements. Such bilateral rhythmic movement patterns are neuronally generated at levels of the spinal cord and brain stem, that are strongly interconnected with cortical circuitries, including the Supplementary Motor Area (SMA).

Objective

To explore cerebral activity associated with multi-limb phase relations in human gait by manipulating mutual attunement of the upper and lower limb antiphase patterns.

Methods

Cortical activity and gait were assessed by ambulant EEG, accelerometers and videorecordings in 35 healthy participants walking normally and 19 healthy participants walking in amble gait, where upper limbs moved in-phase with the lower limbs. Power changes across the EEG frequency spectrum were assessed by Event Related Spectral Perturbation analysis and gait analysis was performed.

Results

Amble gait was associated with enhanced Event Related Desynchronization (ERD) prior to and during especially the left swing phase and reduced Event Related Synchronization (ERS) at final swing phases. ERD enhancement was most pronounced over the putative right premotor, right primary motor and right parietal cortex, indicating involvement of higher-order organization and somatosensory guidance in the production of this more complex gait pattern, with an apparent right hemisphere dominance. The diminished within-step ERD/ERS pattern in amble gait, also over the SMA, suggests that this gait pattern is more stride driven instead of step driven.

Conclusion

Increased four-limb phase complexity recruits distributed networks upstream of the primary motor cortex, primarily lateralized in the right hemisphere. Similar parietal-premotor involvement has been described to compensate impaired SMA function in Parkinson’s disease bimanual antiphase movement, indicating a role as cortical support regions.

Experimental Protocol to Assess Neuromuscular Plasticity Induced by an Exoskeleton Training Session

Exoskeleton gait rehabilitation is an emerging area of research, with potential applications in the elderly and in people with central nervous system lesions, e.g., stroke, traumatic brain/spinal cord injury. However, adaptability of such technologies to the user is still an unmet goal. Despite important technological advances, these robotic systems still lack the fine tuning necessary to adapt to the physiological modification of the user and are not yet capable of a proper human-machine interaction. Interfaces based on physiological signals, e.g., recorded by electroencephalography (EEG) and/or electromyography (EMG), could contribute to solving this technological challenge. This protocol aims to: (1) quantify neuro-muscular plasticity induced by a single training session with a robotic exoskeleton on post-stroke people and on a group of age and sex-matched controls; (2) test the feasibility of predicting lower limb motor trajectory from physiological signals for future use as control signal for the robot. An active exoskeleton that can be set in full mode (i.e., the robot fully replaces and drives the user motion), adaptive mode (i.e., assistance to the user can be tuned according to his/her needs), and free mode (i.e., the robot completely follows the user movements) will be used. Participants will undergo a preparation session, i.e., EMG sensors and EEG cap placement and inertial sensors attachment to measure, respectively, muscular and cortical activity, and motion. They will then be asked to walk in a 15 m corridor: (i) self-paced without the exoskeleton (pre-training session); (ii) wearing the exoskeleton and walking with the three modes of use; (iii) self-paced without the exoskeleton (post-training session). From this dataset, we will: (1) quantitatively estimate short-term neuroplasticity of brain connectivity in chronic stroke survivors after a single session of gait training; (2) compare muscle activation patterns during exoskeleton-gait between stroke survivors and age and sex-matched controls; and (3) perform a feasibility analysis on the use of physiological signals to decode gait intentions.

Neural decoding of gait phases during motor imagery and improvement of the decoding accuracy by concurrent action observation

Objective. Brain decoding of motor imagery (MI) not only is crucial for the control of neuroprosthesis but also provides insights into the underlying neural mechanisms. Walking consists of stance and swing phases, which are associated with different biomechanical and neural control features. However, previous knowledge on decoding the MI of gait is limited to simple information (e.g. the classification of 'walking' and 'rest').Approach. Here, we investigated the feasibility of electroencephalogram (EEG) decoding of the two gait phases during the MI of walking and whether the combined use of MI and action observation (AO) would improve decoding accuracy.Main results. We demonstrated that the stance and swing phases could be decoded from EEGs during MI or AO alone. We also demonstrated the decoding accuracy during MI was improved by concurrent AO. The decoding models indicated that the improved decoding accuracy following the combined use of MI and AO was facilitated by the additional information resulting from the concurrent cortical activations related to sensorimotor, visual, and action understanding systems associated with MI and AO.Significance. This study is the first to show that decoding the stance versus swing phases during MI is feasible. The current findings provide fundamental knowledge for neuroprosthetic design and gait rehabilitation, and they expand our understanding of the neural activity underlying AO, MI, and AO + MI of walking.Novelty and significanceBrain decoding of detailed gait-related information during motor imagery (MI) is important for brain-computer interfaces (BCIs) for gait rehabilitation. This study is the first to show the feasibility of EEG decoding of the stance versus swing phases during MI. We also demonstrated that the combined use of MI and action observation (AO) improves decoding accuracy, which is facilitated by the concurrent and synergistic involvement of the cortical activations for MI and AO. These findings extend the current understanding of neural activity and the combined effects of AO and MI and provide a basis for effective techniques for walking rehabilitation.

Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients

Brain lesions caused by cerebral ischemia lead to network disturbances in both hemispheres, causing a subsequent reorganization of functional connectivity both locally and remotely with respect to the injury. Quantitative electroencephalography (qEEG) methods have long been used for exploring brain electrical activity and functional connectivity modifications after stroke. However, results obtained so far are not univocal. Here, we used basic and advanced EEG methods to characterize how brain activity and functional connectivity change after stroke. Thirty-three unilateral post stroke patients in the sub-acute phase and ten neurologically intact age-matched right-handed subjects were enrolled. Patients were subdivided into two groups based on lesion location: cortico-subcortical (CS, n = 18) and subcortical (S, n = 15), respectively. Stroke patients were evaluated in the period ranging from 45 days since the acute event (T0) up to 3 months after stroke (T1) with both neurophysiological (resting state EEG) and clinical assessment (Barthel Index, BI) measures, while healthy subjects were evaluated once. Brain power at T0 was similar between the two groups of patients in all frequency bands considered (δ, θ, α, and β). However, evolution of θ-band power over time was different, with a normalization only in the CS group. Instead, average connectivity and specific network measures (Integration, Segregation, and Small-worldness) in the β-band at T0 were significantly different between the two groups. The connectivity and network measures at T0 also appear to have a predictive role in functional recovery (BI T1-T0), again group-dependent. The results obtained in this study showed that connectivity measures and correlations between EEG features and recovery depend on lesion location. These data, if confirmed in further studies, on the one hand could explain the heterogeneity of results so far observed in previous studies, on the other hand they could be used by researchers as biomarkers predicting spontaneous recovery, to select homogenous groups of patients for the inclusion in clinical trials.

Cortico-muscular functional network: an exploration of cortico-muscular coupling in hand movements

Objective. The main objective of this research was to study cortico-muscular, intra-cortical, and inter-muscular coupling. Herein, we established a cortico-muscular functional network (CMFN) to assess the network differences associated with making a fist, opening the hand, and wrist flexion.Approach. We used transfer entropy (TE) to calculate the causality between electroencephalographic and electromyographic data and established the TE connection matrix. We then applied graph theory to analyze the clustering coefficient, global efficiency, and small-world attributes of the CMFN. We also used Relief-F to extract the features of the TE connection matrix of the beta2 band for the different hand movements and observed high accuracy when this feature was used for action recognition.Main results. We found that the CMFN of the three actions in the beta band had small-world attributes, among which the beta2 band's small-world was stronger. Moreover, we found that the extracted features were mainly concentrated in the left frontal area, left motor area, occipital lobe, and related muscles, suggesting that the CMFN could be used to assess the coupling differences between the cortex and the muscles that are associated with different hand movements. Overall, our results showed that the beta2 (21-35 Hz) wave is the main information carrier between the cortex and the muscles, and the CMFN can be used in the beta2 band to assess cortico-muscular coupling.Significance. Our study preliminarily explored the CMFN associated with hand movements, providing additional insights regarding the transmission of information between the cortex and the muscles, thereby laying a foundation for future rehabilitation therapy targeting pathological cortical areas in stroke patients.

Visual Attention and Motion Visibility Modulate Motor Resonance during Observation of Human Walking in Different Manners

To advance our knowledge on the motor system during cyclic gait observation, we aimed to explore the effects of gaze fixation on corticospinal excitability evaluated by single-pulse transcranial magnetic stimulation (TMS). Fourteen healthy adult volunteers watched a video of a demonstrator walking on a treadmill under three different conditions: (1) observing the right lower limb, (2) observing the right ankle joint, and (3) observing the right lower limb on a video focused on the area below the knee. In each condition, motor-evoked potentials elicited by TMS in the tibialis anterior (TA) muscle were measured synchronously with the demonstrator’s initial contact and toe-off points. Directing visual attention to the ankle joint and focusing on its movements caused corticospinal facilitation in the TA muscle compared with watching the video without any visual fixation. In addition, phase-dependent differences in corticospinal excitability between the initial contact and toe-off points were only detected when the visibility range was restricted to below the knee. Our findings indicated that motor resonance during cyclic gait observation is modulated by visual attention and motion visibility in different activation manners.

Surface Electromyography and Electroencephalogram-Based Gait Phase Recognition and Correlations Between Cortical and Locomotor Muscle in the Seven Gait Phases

The classification of gait phases based on surface electromyography (sEMG) and electroencephalogram (EEG) can be used to the control systems of lower limb exoskeletons for the rehabilitation of patients with lower limb disorders. In this study, the slope sign change (SSC) and mean power frequency (MPF) features of EEG and sEMG were used to recognize the seven gait phases [loading response (LR), mid-stance (MST), terminal stance (TST), pre-swing (PSW), initial swing (ISW), mid-swing (MSW), and terminal swing (TSW)]. Previous researchers have found that the cortex is involved in the regulation of treadmill walking. However, corticomuscular interaction analysis in a high level of gait phase granularity remains lacking in the time–frequency domain, and the feasibility of gait phase recognition based on EEG combined with sEMG is unknown. Therefore, the time–frequency cross mutual information (TFCMI) method was applied to research the theoretical basis of gait control in seven gait phases using beta-band EEG and sEMG data. We firstly found that the feature set comprising SSC of EEG as well as SSC and MPF of sEMG was robust for the recognition of seven gait phases under three different walking speeds. Secondly, the distribution of TFCMI values in eight topographies (eight muscles) was different at PSW and TSW phases. Thirdly, the differences of corticomuscular interaction between LR and MST and between TST and PSW of eight muscles were not significant. These insights enrich previous findings of the authors who have carried out gait phase recognition and provide a theoretical basis for gait recognition based on EEG and sEMG.

Analysis of corticomuscular connectivity during walking using vine copula

Corticomuscular connectivity plays an important role in the neural control of human motion. This study recorded electroencephalography (EEG) and surface electromyography (sEMG) signals from subjects performing specific tasks (walking on level ground and on stairs) based on metronome instructions. This study presents a novel method based on vine copula to jointly model EEG and sEMG signals. The advantage of vine copula is its applicability in the construction of dependency structures to describe the connectivity between the cortex and muscles during different movements. A corticomuscular function network was also constructed by analyzing the dependence of each channel sample. The successfully constructed network shows information transmission between different divisions of the cortex, between muscles, and between the cortex and muscles when the body performs lower limb movements. Additionally, it highlights the potential of the vine copula concept used in this study, indicating that significant changes in the corticomuscular network under lower limb movements can be quantified by effective connectivity values.

Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study

Background

The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients.

Methods

We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients.

Results

The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group.

Conclusions

The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

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.