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

Abstract

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.

1. Introduction

About 28% of American adults older than 50 in the general community presented with impaired locomotion and its prevalence increased with age (p < 0.001) (Mahlknecht et al., 2013). The causes of impaired locomotion are divided into neurological (e.g., Parkinson's disease, stroke, Multiple Sclerosis, and dementia) (Allali et al., 2018; Buckley et al., 2019) and/or musculoskeletal drivers, such as arthritis and cardiovascular conditions (Blyth et al., 2019; Andonian and Huffman, 2020; Minetto et al., 2020). Among these neurological and musculoskeletal impacts, previous research has identified different patterns of gait disorders, such as parkinsonian (De Bartolo et al., 2020; Guayacán and Mart́ınez, 2021), frontal (Hülser et al., 2022), or spastic gait (Muñoz-Lasa et al., 2019; Norbye et al., 2020). Furthermore, researchers have argued that locomotion should be defined as a syndrome for pre-clinical outcomes, such as motoric cognitive risk syndrome for pre-dementia (Xiang et al., 2021; Li et al., 2022a). Because of the neurological and musculoskeletal correlates of locomotion, rehabilitation treatments have been explored to utilize locomotion training, such as treadmill gait exercise, to improve patients' musculoskeletal ability and further induce neurological benefits (Smania et al., 2011; Hornby et al., 2016; Bassiri et al., 2022). Therefore, each human locomotion contains unique features of neural and musculoskeletal drivers, clinical conditions, the complexities of human development and aging, and signifiers of physical activity for health and wellness (Runge and Hunter, 2006; Pons et al., 2013; Kerkman et al., 2018).

Understanding the neurological and musculoskeletal correlates of locomotion is a pivotal need to pave the way for successful rehabilitation, improve performing locomotion tasks, especially in older adults, or create a digital twin of humans while doing a locomotion task (Dai et al., 2022). Various techniques, such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and magnetic resonance imaging (MRI), have been developed and deployed to study age-related changes and specific diseases in the neurological and musculoskeletal correlates (Lewis et al., 2019). These various techniques enable us access to the movement phenotypes, such as brain structures (Chenausky and Tager-Flusberg, 2022), the functional substrates (Magrinelli et al., 2021), and motion signatures (Klibaite et al., 2022), in locomotion control during different experimental settings. However, these neuroimaging techniques present advantages and limitations, such as sensitivity to motion artifacts (Abtahi et al., 2020; Bonnal et al., 2022), portability (Sejdić et al., 2019), and spatial and temporal resolutions (Martini et al., 2020; Kumar and Michmizos, 2022). These limitations set up constraints on the design of the experimental protocols. For instance, MRI studies mainly investigated imagined locomotion rather than real-world locomotion (Stolbkov et al., 2019; Skinner et al., 2022). Although the sensitivity of fNIRS and EEG to artifacts has been improved, still most existing studies focus on low-intensity movement (e.g., walking) rather than high intensity, such as running.

To overcome these challenges and limitations of traditional brain imaging techniques, most recent research argued that brain-body imaging techniques that enable simultaneous measurements of dynamics of brain activities and body movements could be a promising method to reveal the profound relationship between brain, body, and behavior (Makeig et al., 2009; Gramann et al., 2010; Gwin et al., 2010; Wagner et al., 2012). The brain/body imaging techniques integrated portable devices, reliable sensing methods against motion artifacts, and capable of monitoring brain activities and locomotion with appropriate temporal and spatial resolutions, and sophisticated data analysis approaches for multi-modal data preprocessing and curation. Thus far, some specialized centers and clinics such as GE HealthCare (HealthCare, 2023), SCL Health (Group, 2023), Stratus (Stratus, 2023), Zeto (Zeto, 2023), CMS (CMS, 2023), CNS (Calyx, 2023), NordicNeuroLab (NordicNeuroLab, 2023), and NIRx (NIRx, 2023) have adopted these brain/body imaging tools. With multiple-channel EEG (more than 64) capturing brain activities, these tools track spatial information of brain correlates of human motion and provide high-precision data at a sampling rate of 200–5,00 Hz (Cortney Bradford et al., 2019). Simultaneously, they utilized standard locomotion tools, such as optical motion capture systems (Divya and Peter, 2022), inertial measurement units (Khaksar et al., 2021), and EMG sensors (Hallett et al., 2021) to capture the locomotion data at the same sampling rate as brain activity data. As a result, sophisticated data analysis approaches have been developed to study the relationship between brain, body, and behavior, such as cognitive-motor interference and coherence (Zhu et al., 2022).

However, before these brain-body imaging tools can truly be adopted for clinical use, their effectiveness must be carefully assessed. From a technical point of view, reproducing human locomotion experiments has been problematic due to the lack of protocols, standardization, and benchmarking tools, which ultimately impairs the evaluation of previous research quality and validation of prior understandings (Parmentier et al., 2020; Kameli et al., 2021). Notably, only a few studies focused on developing standard protocols and related design methodology as an open research issue. Therefore, this paper focuses on reviewing the settings of existing experiment protocols, such as locomotion intensity, duration, distance, brain sensor technologies, and corresponding brain activation expressions. First, we develop a conceptual framework to identify the design methodology of experiment protocols. Limitations of each brain-body imaging technique and the corresponding constraints on protocol design are then characterized and mapped into the scope of the systematic review approach. Next, we review existing studies that implement various types of experiment protocols, demonstrating the current gaps in design methodology. Finally, metrics for evaluating the research quality and implications for reproducing prior knowledge are proposed.

Overall, we aim to provide a roadmap for the future development of locomotion analysis methods based on brain-body imaging techniques, including highlighting current progress, identifying various constraints, and suggesting potential research directions. The main contributions of this review paper are to:

• Establish intrinsic links between neurological and musculoskeletal correlates of locomotion characteristics and quantifiable measures that brain-body imaging tools can capture;

• Review existing experimental protocols for studying neurological and musculoskeletal correlates of locomotion;

• Examine the feasibility of replicating the experiments in the laboratory systems, and finally,

• Identify gaps and lay out a roadmap for the design methodology of experiment protocols.

2. Methodology

The design methodology of experimental protocols for studies on neurological and musculoskeletal correlates of human locomotion is based on careful considerations of cognitive and locomotive demands, observation constrained by the sensing technology, the pathology derived from the research interests, and energetic costs invested by the human subjects. Ideally, the cognitive and locomotive demands are designed in specific thresholds to stimulate certain intertwined relationships between neurological and/or musculoskeletal activities. However, the sensing technology's thresholds of these demands are constrained, including sensitivity to motion artifacts, portability in different environments, and spatial and temporal resolutions for capturing dynamics. For instance, current sensing techniques, such as EEG and fNIRS, claim they have lowered their sensitivity to motion artifacts. Still, most researchers design low-intensity locomotion (e.g., slow walking) to avoid difficulty in denoising efforts. Sometimes, in high-intensity locomotion experiments (e.g., running), the data become useless due to a high amount of movement artifacts (Gwin et al., 2011). Figure 1 illustrates the conceptual framework of the design methodology of experimental protocols.

Figure 1

We developed a systematic search of existing studies based on the conceptual framework. To find the papers, these keywords were searched for in Google Scholar: human brain locomotion/locomotion, OR EEG, OR nirs, OR MRI/ Brain-Body Imaging. Then, all the papers were examined, and the related papers were added to the tables of this paper. The criteria for a paper to be reviewed are: 1) it must examine the brain's signals while the participants perform a locomotion task. 2) The experiment participants should be healthy; the results of this systematic search are shown in Figure 2. Although exploring walking disorders is highly valuable, there are thorough reviews for each locomotion disease. Then, the critical information on protocol design has been extracted and inserted into the tables. In this respect, these parameters in each protocol have been extracted: the type of the surface or the device used for experimenting (e.g., overground and treadmill), the speed of performing the task, the distance that participants have moved, the duration of the task, the type of the sensors used in the experiment (it is more focused on the sensors to read the brain's data), number and age of the participants, special conditions of each research, and the contribution of the research.

Figure 2

All the results were converted to the same units to make the protocols comparable. Accordingly, the distance is stated in meters (m), the speed is in kilometers per hour (km/h), and the duration of the task is in minutes (min); otherwise, the unit is stated.

3. Locomotive and cognitive demands

To examine neural activities during human locomotion, single tasks without cognitive demands (e.g., walking) and dual tasks with cognitive demands (e.g., talking while walking) are examined. In this paper, the tasks are divided into walking, running, cycling, and Dual-Task-Walking (DTW). The abbreviations used in this paper are explained in Table 1.

• In the participant section, M shows the number of male participants and the rest are Females.

• a ± b is used to describe the participants' age statistics in which a is mean and b is standard deviation.

• If participants are older than 60, they are considered as OA.

3.1. Type 1: Walking

Walking has been examined in different forms: actual walking, imaginary walking, and simulated walking. Nonetheless, the common point between these studies is the activation of the brain and involvement of higher cognition control areas (Al-Yahya et al., 2011). The studies that have investigated the walking as the human locomotion are shown in Table 2.

3.2. Type 2: Running

Only one paper was found that merely focused on examining running as a locomotion task and studying brain activities. This paper's information is described in Table 3.

3.3. Type 3: Cycling

Cycling is another locomotive task to study the brain correlates of locomotion. The locomotive demands span from low to high intensity, with various cycling speeds, while few studies gave self-paced instructions to participants. Most studies expected to utilize the locomotive demands to stimulate the dynamics of brain activities for identifying neurological and musculoskeletal benefits. The research studies related to the cycling are demonstrated in Table 4.

3.4. Type 1-2: Walking-running

In this section, three research were found that have investigated both walking and running their studies, which meet the defined criteria for research selection in this review. These studies are investigated in Table 5.

3.5. Type 1–2: Walking-cycling

Only one paper shown in Table 6 was found that has examined both walking and cycling.

3.6. Type 4: Dual walking task-spontaneous locomotive and cognitive demands

By switching from single tasks to dual tasks, the age-related gait changes are more distinguished (Beurskens and Bock, 2012) the reason roots in the fact that the cognitive resources should compensate for the motor impairments (Mirelman et al., 2017). Because of this reason, in this type of locomotion task, usually both young adults (YA) and old adults (OA) are examined. The Dual task walking studies are presented in Table 7.

4. Results

4.1. Task analysis

The filtered papers based on the mentioned criteria are 102 papers. Most of the papers, 65, conducted research on low-intensity locomotion, walking, only 4 papers examined running, 17 papers examined cycling, and 18 papers conducted the experiment on dual task, which considers the locomotive and cognitive demands simultaneously. Figure 3 shows the visualization of the tasks, the surface that the experiment has been conducted on, and the brain sensor that has been employed for the experiment. In this regard, the most outer layer of the circle shows the investigated locomotion tasks (walking, running, cycling, and dual-task walking). The middle circle demonstrates the type of the surface (Tr: treadmill, IM: imaginary, OG: overground, Erg: Ergometer bicycle, B: on a board). The type of brain sensor used in the research are shown in the most inner circle (EEG, NIRS, MRI, and PET).

Figure 3

4.2. Analysis of locomotive intensity

Among the researches that have declared the intensity (e.g., speed) that locomotion has been performed, the papers that have stated their speed (or an average of speed) have been compared with each other. However, the researches that the speed was not defined quantitatively and constant (e.g., the speed was dependent on the length of the participants' feet length Wagner et al., 2012, 2014; Seeber et al., 2014) were excluded from the visualization. Accordingly, the rotational velocities when the brain-body imaging experiment is using an ergometer or a pedaling board are visualized in Figure 4. The experiment using 60 rpm in cycling has been considered a reference speed for most researchers. Besides the rotational velocities of Figure 4, the linear speed for SW and DTW on different surfaces (i.e., Tr and OG) for YA and OA are shown in Figure 5. The bigger the bulb, the more repetition on reported speed. For example, walking at the speed of 2 Km/h is the main standard for researchers on the treadmill when the participants are YA. In addition, in each experiment surface, a trend of using a higher speed for YA compared to the speed used for OA can also be observed in the reviewed papers in this figure. For instance, in DTW OG, the maximum speed reported for YA is 4.6 Km/h, although the maximum speed reported for OA in DTW OG is about 3.8 Km/h.

Figure 4

Figure 5

4.3. Analysis of participants' age

To analyze the age of the participants, when the age of the participant is unknown, the research is excluded in the visualization of Figure 6. In this regard, there is a significant gap in the ages between 40 and 60 years old as only studies had participants in this range of age.

Figure 6

4.4. Analysis of locomotion duration

The duration of locomotion task varies from seconds to hours (Figure 7). In this regard, when it comes to single walking, the experiment on the treadmill with a duration of 12 min is the primary source for experiments. To make the results of studies with different duration comparable, the effect of fatigue on the muscles needs to be studied, and one solution could be recording the data after the locomotion task for a specific time.

Figure 7

4.5. Analysis of locomotion distance

Besides the duration, another parameter for assessing the locomotion demands is the distance when the participants were asked to walk overground. The walk distance falls in a range of 1 to 150 meters. For better demonstration, only the distances up to 30 meters are shown in Figure 8.

Figure 8

4.6. Brain activation in brain-body imaging experiments

In this section, the corresponding brain activation to the locomotion is presented. Comparison of the brain activation areas for the locomotion tasks are shown in Figure 9. In this figure, we have only shown the results of the reviewed papers that have specifically mentioned the brain regions' activation sources and their effects. However, if a study generally describes the cortex area is excluded from this visualization. In this figure, ∝ is the proportional symbol, ↑ shows increasing, and ↓ shows reduction of an item. For instance, when the OA's walk speed increases, oxyHB in the Supplementary Motor Area increases.

Figure 9

5. Discussion

In this section, the configurations of locomotive and cognitive demands, the research interests regarding neurological and musculoskeletal drivers, and the observational constraints from the sensing techniques are discussed concerning the impact of brain-body imaging sensors on the design methodology of experimental protocols for measuring dynamics of brain, body, and behavior. Since few papers in the field have adopted a conceptual framework to evaluate the quality of the neurological and musculoskeletal correlates of human locomotion extracted using various methods, it is not easy to compare these state-of-the-art methods. Therefore, in this section, we focus on establishing the elements of the proposed conceptual framework and discuss how to assess the neurological and musculoskeletal measures extracted from brain-body imaging sensors for further clinical use.

5.1. Neurological and musculoskeletal drivers

According to our systematic review results, most studies followed the philosophy of medical diagnosis that is rooted in conducting statistical analysis between different groups (e.g., young vs. old, control vs. patient). These groups were asked to perform specific behavioral protocols under which their performances were supposed to show neurological and musculoskeletal differences reflected by the brain-body imaging data. Therefore, various experimental protocols were explored to capture the differences in statistical norms among groups (Kashuba et al., 2020; Warmerdam et al., 2020). The typical statistical analysis approaches include comparisons of statistical norms (e.g., p-value, effect size) and classifications based on machine learning (e.g., accuracy, precision) (Figueiredo et al., 2018; Hatami et al., 2019; Patil et al., 2019; Hausmann et al., 2021). Few studies adopted representation learning methods based on deep neural networks to explore patterns and features of brain and motion signals to show the group differences (Vásquez-Correa et al., 2018; Talo et al., 2019; Song et al., 2021). Furthermore, specific quantitative assessments, such as clinical outcomes or symptoms, were identified as ground truth so that the same reference could examine both groups. However, no consensus on experimental protocols caused challenges in determining reliable ground truth or references. Otherwise, few studies provided explanations and rationales for the design methodology of behavioral experiments.

Although it lacks standardization of experimental protocols, existing studies still generated consistent conclusions on neurological and musculoskeletal correlates of human locomotion. For example, most studies revealed that the active brain regions during various walking protocols include the motor, sensory, and prefrontal cortexes. The brain areas involved in walking behavior span SMA, premotor cortex, sensorimotor, M1, and left and right prefrontal cortex. Notably, the age-related changes showed that the involvement of PFC increased among old adults, especially during high-speed walking. When the subjects need to adjust their postures for cycling, the involvement of PFC decreases in order to keep mental resources for the maintenance of these additional requirements during exercise. The PFC has been implicated in planning complex cognitive behavior, especially in the resting status. Few studies explored the running protocols; therefore, there is little consistent knowledge regarding neurological and musculoskeletal correlates of human running. It is noteworthy that when subjects are required to conduct spontaneous cognitive tasks, the PFC activation increases in all age populations, and its increase is more significant in older adults with and without cognitive impairment. These conclusions have motivated many hypothesis-driven research projects on disease-related changes in neurological and musculoskeletal correlates of human locomotion.

Besides the consistent understanding of neurological correlates of human locomotion, mixed results and conclusions exist due to the heterogeneity of participants' neurological expressions, musculoskeletal variance under imprecise experimental protocols, and observation constraints by the sensing techniques. For instance, some studies concluded that the cerebral cortex controls multiple muscles hierarchically through a few synergies during walking. In contrast, few studies argued that the role of cortical control during walking might not be valid due to the motion artifacts of EEG. Furthermore. Subjects that conducted actual walking were more accurately classified than subjects with imaginary walking. Subjects that walked on a treadmill showed different band activities captured by EEG compared to subjects who walked over the ground. These inconsistent results require further investment in establishing protocols, standardization, and benchmarking tools, which also motivated this research work.

5.2. Locomotive and cognitive demands

The primary assumption underlying the experimental protocols is that the locomotive and cognitive demands could stimulate changes and patterns in neural activity, which the brain-body sensors could pick up (Ladouce et al., 2019). Thus, the knowledge of the simulation mechanisms further guides the treatment and rehabilitation strategies for potential clinical outcomes. However, most experimental protocols involve low-intensity locomotion tasks, such as walking and cycling, while few studies conducted high-intensity locomotion tasks, such as running. In addition, little research explained the design methodology of the experimental protocol, especially the research hypothesis of which types of neural activities might be expected under the experimental configurations, such as duration, intervals, and frequency. Also, conducting behavioral studies on human subjects is always challenging, even more, if considering psychological factors such as state of mind, concentration, and technical dexterity. Therefore, some studies integrated cognitive demands into locomotive tasks, such as avoiding obstacles, determining walking directions, or following an avatar. Other studies developed a dual-task paradigm to examine the involvement of cognition in human locomotion.

As illustrated in Figure 1, the conceptual framework argued that more work is needed to examine the simulation mechanisms, thus developing a better design methodology for experimental protocols. The systematic review results showed that several challenging questions remain in the research field. First, benchmarking the locomotive and cognitive demands will be needed. Locomotive tasks have several configurable parameters, such as duration, intervals, and frequency, which need to divide into several levels or intensities. Cognitive tasks also have configurable parameters, such as type and complexity, depending on working memory and executive functions. For instance, for a specific subpopulation's demographic, locomotion and cognition capacity, researchers in designing experimental protocol need a basic understanding of which levels of demands might stimulate the anticipated brain activity. Knowledge of locomotion disorder patterns and corresponding brain areas has helped establish experimental protocols for several neurological diseases, such as parkinsonian gait for Parkinson disease (Ghai et al., 2018) and NIH cognitive toolbox for cognitive impairment (Gershon et al., 2010). Second, manipulation of the tasks needs more research investment. Existing studies rarely consider how the sequential configuration of the tasks stimulates neural activity. Most protocols conducted a heuristic-designed sequence of locomotive tasks and anticipated the brain-body sensors could capture the subtle changes or patterns of neural activities. However, the loop from demands to brain and musculoskeletal activities to sensors, in Figure 1, shows that manipulation of the demands could generate richer information than the heuristic-designed protocol. Third, the experimental protocols should be easily administered to avoid confusion and distraction for participants. Few studies gave a clear description of how the protocols are being instructed. Cueing the participants toward specific tasks could have influenced the expected neural activity and cognitive performance. Therefore, most researches argued that when the experiment protocol is administered and instructed by assistance of a computer is less likely that neural activity and cognitive performance get adversely affected compared to the case that the experiment is controlled and instructed by interference of a human. Hence, replication of previous experiment protocols that were controlled by a human as examiner with a computer-assisted administration provides a high-quality data with removal of the human interference affects (Vrana and Vrana, 2017; Dror, 2020; Young et al., 2022). Moreover, computer assistance in the cognitive load assessment could make it feasible to conduct the experiment at places out of the clinics and laboratories. These places (e.g., participants' home or a local clinic) are accessible to the participants and individuals are comfortable to perform the experiment with assistance of a computer without interference of an examiner. As positive side effects of the computer-assisted experiment administration, high-quality and cost-effective patient care could be provided (Porrselvi, 2022; Young et al., 2022).

5.3. Observation constraints by the sensing techniques

Another dimension of our systematic review results is illustrating the impacts of the observation constraints by the brain-body sensing techniques. Previous work has reviewed and discussed the advantages and disadvantages of these techniques, including tolerance of motion artifacts and spatial and temporal resolutions. However, most studies reviewed in this work did not explain the rationale for selecting specific sensing techniques in experimental protocol design. According to our review results, it is obvious to see the impacts of sensing techniques on the research results and generated knowledge. Therefore, we summarize these impacts for enabling guidelines for future researchers to design experimental standards and benchmarks.

5.3.1. Tolerance of motion artifacts

Prior knowledge of sensing techniques has concluded that among three of them, MRI has the lowest tolerance to motion artifacts, EEG less, and fNIRS the highest. Despite efforts in signal processing and denoising to improve each technique's tolerance to motion artifacts, most studies still followed the knowledge and showed different experimental results. Studies that adopted MRI has considered its limited tolerance of motion artifacts and mainly designed imaginary locomotion or simulated surrogate tasks rather than actual locomotion in their experiments (Stolbkov et al., 2019; Amemiya et al., 2021). The central assumption of these studies is that neurological correlates of these imaginary or surrogate tasks are closely related to neurological activation during actual locomotion. However, our summarized results in locomotion tables and Figure 9 showed that these imaginary or surrogate tasks could not simulate the complex dynamics the brain must execute to adjust and maintain the musculoskeletal patterns during actual locomotion. Especially studies that utilized EEG have shown that actual walking stimulated brain activation patterns that could be classified with higher precision than imagery walking. Therefore, studies that adopted EEG sensors designed a more comprehensive range of locomotion tasks, from imaginary walking and cycling to running. Furthermore, the fNIRS studies are preferred in high-intensity locomotion experiments. More than 50% of studies reviewed in this work that conducted cycling and running tasks adopted fNIRS sensors.

Sensitivity of EEG data to motion artifacts is a research concern and the results of some previous published research due to not considering an extensive removal of motion artifacts has been questioned. For instance, the reported EEG results indicating the changes in high-gamma frequency band during walking (Gwin et al., 2011) could be caused by motion artifacts as well (Castermans et al., 2014). To address the considerable drawback, different denoising methods to employ during or after data collection have been developed. During data collection, artifact removal is associated with hardware modifications. In this respect, one effective way has been introduced to separate electrophysiological signals from non-neural signals. To this end, in one approach, two layers are used below the EEG cap. A silicone layer is used on top of the scalp to block electrophysiological signal. Then, a simulated conductive scalp with similar impedance to human scalp is used to measure the voltage differences generated by gait dynamics (Snyder et al., 2015). In another approach, well-known as a EEG dual electrode design, simultaneously EEG data and isolated motion artifacts are recorded by pairs of the electrodes that are electrically independent and mechanically coupled (Nordin et al., 2018; Clark et al., 2020). After the data collection, denoising is coupled with software data processing. In this regard, Independent Component Analysis (ICA), low, high, and band band pass filter are routinely applied. Besides these remedies, ICA-based methods such as adaptive ICA mixture model algorithm (Palmer et al., 2006), extended infomax ICA (Lee et al., 1999) and multiple mixture ICA (Allen et al., 2000) approaches are utilized (Gwin et al., 2010). Moreover, a developed conductive head phantom and robotic motion platform has emerged as a powerful tool to analyze the artifact removal methods through generating a ground truth for EEG signal. This device is used to evaluate artifact removal methods such as dual-layer EEG and Artifact Subspace Reconstruction (Richer et al., 2020). Also, this device is used to show that electrodes with larger surface reduces the electrodes vulnerability to motion artifacts (Symeonidou et al., 2018). In addition, this device is employed to assess the effect of the motion artifacts parameters such as frequency and amplitude (Oliveira et al., 2016).

5.3.2. Spatial and temporal resolutions

MRI has the highest spatial resolution among the three sensing techniques while fNIRS has the lowest (only centimeters under the skull) (Li et al., 2022b). Accordingly, MRI studies discussed their results with a detailed description of the brain cortex, such as SMA and the dorsal premotor cortex. Nonetheless, lacking the portability feature has significantly limited the application of the MRI in the brain-body imaging of locomotion studies mostly to imaginary task and thus, this scope of study has been deprived from the high spatial resolution of the MRI imaging technique. On the other hand, although EEG has lower spatial resolution than MRI, appropriate source localization approaches could improve its spatial resolution at a cortical level. When source localization is used, the spatial resolution of the fNIRS and EEG are comparable. However, EEG has the highest temporal resolution among the three methods and permits the highest and the most precise data brain investigation compared to the fNIRS and MRI methods. Thus, Looking into the results summarized in our work, we can see that studies that adopted EEG sensors provided the neurological and musculoskeletal correlates on a fine-grained temporal scale, such as neural activation patterns during different movement periods (e.g., swing, stance). These features has made the EEG the most popular technique in the explorations of the brain-body imaging of human locomotion.

5.3.3. Miscellaneous constraints

We also observed other constraints in the design methodology of experimental protocols, such as imbalanced ages of participants. For example, most studies recruited participants aged below 40, while fewer recruited participants older than 60. A significant age gap (40–60) among the participants involved in these studies existed. However, the adults within this age range are baby boomers in the U.S., a large sector of the population, a group deemed by the Centers for Disease Control and Prevention (CDC) as mid-life stage adults. This age population is facing an increase in clinical and other preventive services to support maintaining good health into older age (Pearson-Stuttard et al., 2019; Doubeni et al., 2021), which means this age group should be a critical population for studies on neurological and musculoskeletal correlates of human locomotion. Although there might exist real-world challenges to recruiting this specific age population, additional investment in balancing the age distribution of the participants is needed to fulfill a cross-lifespan understanding of human locomotion and its neurological and musculoskeletal correlates.

6. Conclusion

This paper explores the topic of the design methodology of experimental protocols that aim to study neurological and musculoskeletal correlates of human locomotion using brain-body sensing techniques. The review of many types of neural activities stimulated by human locomotion demonstrates the importance of quantitative analysis using brain-body sensors in potential healthcare applications. By reviewing the current design methodology of experimental protocols, this paper illustrates that the protocol design significantly impacts the experimental results due to the heterogeneity of participants' neurological expressions, musculoskeletal variance under the imprecise locomotive and cognitive demands, and observation constraints by the sensing techniques. Finally, the impacts of the experimental protocols are discussed by reviewing the practical issues to provide implications and guidelines for future researchers to design experimental standards and benchmarks.

Brain-body imaging of human locomotion is a vast area of research. This paper focused on a significant research issue: how to reproduce human locomotion experiments. Therefore, we conducted a systematic review of existing experiment protocols to examine various settings and conditions, such as locomotion intensity, locomotion duration, locomotion distance, brain sensing technologies, and corresponding brain activation expressions. In future work, technologies for locomotion sensing and their advantages and disadvantages will be further examined and discussed. Also, upper-limb locomotion, such as shoulder, elbow, wrist, and finger movement, is a broad study that will be examined in another work. Finally, as the participants of this systematic review are healthy, similar brain-body imaging experiment exploration for neuromechanical disorders would be a valuable work to extend.

References

• 1

  Abtahi M. Borgheai S. B. Jafari R. Constant N. Diouf R. Shahriari Y. et al . (2020). Merging fnirs-eeg brain monitoring and body motion capture to distinguish parkinsons disease. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 1246–1253. 10.1109/TNSRE.2020.2987888

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 2

  Alchalabi B. Faubert J. Labbe D. R. (2019). EEG can be used to measure embodiment when controlling a walking self-avatar, in 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (Osaka: IEEE), 776–783.

  • Google Scholar

• 3

  Allali G. Blumen H. M. Devanne H. Pirondini E. Delval A. Van De Ville D. (2018). Brain imaging of locomotion in neurological conditions. Neurophysiol. Clin. 48, 337–359. 10.1016/j.neucli.2018.10.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4

  Allen P. J. Josephs O. Turner R. (2000). A method for removing imaging artifact from continuous eeg recorded during functional mri. Neuroimage12, 230–239. 10.1006/nimg.2000.0599

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5

  Al-Yahya E. Dawes H. Smith L. Dennis A. Howells K. Cockburn J. (2011). Cognitive motor interference while walking: a systematic review and meta-analysis. Neurosci. Biobehav. Rev. 35, 715–728. 10.1016/j.neubiorev.2010.08.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6

  Amemiya K. Morita T. Hirose S. Ikegami T. Hirashima M. Naito E. (2021). Neurological and behavioral features of locomotor imagery in the blind. Brain Imaging Behav. 15, 656–676. 10.1007/s11682-020-00275-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  Andonian B. J. Huffman K. M. (2020). Skeletal muscle disease in rheumatoid arthritis: the center of cardiometabolic comorbidities?Curr. Opin. Rheumatol. 32, 297–306. 10.1097/B.O.R.0000000000000697

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8

  Artoni F. Fanciullacci C. Bertolucci F. Panarese A. Makeig S. Micera S. et al . (2017). Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking. Neuroimage159, 403–416. 10.1016/j.neuroimage.2017.07.013

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9

  Bassiri Z. Austin C. Cousin C. Martelli D. (2022). Subsensory electrical noise stimulation applied to the lower trunk improves postural control during visual perturbations. Gait Posture96, 22–28. 10.1016/j.gaitpost.2022.05.010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10

  Berger A. Horst F. Steinberg F. Thomas F. Müller-Eising C. Schöllhorn W. I. et al . (2019). Increased gait variability during robot-assisted walking is accompanied by increased sensorimotor brain activity in healthy people. J. Neuroeng. Rehabil. 16, 1–13. 10.1186/s12984-019-0636-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  Beurskens R. Bock O. (2012). Age-related deficits of dual-task walking: a review. Neural Plast. 2012, 608. 10.1155/2012/131608

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12

  Billaut F. Kerris J. P. Rodriguez R. F. Martin D. T. Gore C. J. Bishop D. J. (2013). Interaction of central and peripheral factors during repeated sprints at different levels of arterial O₂ saturation. PLoS ONE8, e77297. 10.1371/journal.pone.0077297

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  Blyth F. M. Briggs A. M. Schneider C. H. Hoy D. G. March L. M. (2019). The global burden of musculoskeletal pain–where to from here?Am. J. Public Health109, 35–40. 10.2105/AJPH.2018.304747

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14

  Bonnal J. Monnet F. Le B.-T. Pila O. Grosmaire A.-G. Ozsancak C. et al . (2022). Relation between cortical activation and effort during robot-mediated walking in healthy people: a functional near-infrared spectroscopy neuroimaging study (fnirs). Sensors22, 5542. 10.3390/s22155542

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  Bradford J. C. Lukos J. R. Ferris D. P. (2016). Electrocortical activity distinguishes between uphill and level walking in humans. J. Neurophysiol. 115, 958–966. 10.1152/jn.00089.2015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16

  Brantley J. A. Luu T. P. Ozdemir R. Zhu F. Winslow A. T. Huang H. et al . (2016). Noninvasive eeg correlates of overground and stair walking, in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Orlando, FL: IEEE), 5729–5732.

  • Pubmed Abstract
  • Google Scholar

• 17

  Bruijn S. M. Van Dieën J. H. Daffertshofer A. (2015). Beta activity in the premotor cortex is increased during stabilized as compared to normal walking. Front. Hum. Neurosci. 9, 593. 10.3389/fnhum.2015.00593

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 18

  Buckley C. Alcock L. McArdle R. Rehman R. Z. U. Del Din S. Mazzà C. et al . (2019). The role of movement analysis in diagnosing and monitoring neurodegenerative conditions: Insights from gait and postural control. Brain Sci. 9, 34. 10.3390/brainsci9020034

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 19

  Budde H. Wegner M. Soya H. Voelcker-Rehage C. McMorris T. (2016). Neuroscience of exercise: neuroplasticity and its behavioral consequences. Neural Plast. 2016, 3643879. 10.1155/2016/3643879

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20

  Bulea T. C. Kim J. Damiano D. L. Stanley C. J. Park H.-S. (2015). Prefrontal, posterior parietal and sensorimotor network activity underlying speed control during walking. Front. Hum. Neurosci. 9, 247. 10.3389/fnhum.2015.00247

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21

  Calyx (2023). ©Calyx. Available online at: https://www.calyx.ai/ (accessed February 14, 2023).

  • Google Scholar

• 22

  Castermans T. Duvinage M. Cheron G. Dutoit T. (2014). About the cortical origin of the low-delta and high-gamma rhythms observed in eeg signals during treadmill walking. Neurosci. Lett. 561, 166–170. 10.1016/j.neulet.2013.12.059

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23

  Castermans T. Duvinage M. Petieau M. Hoellinger T. De Saedeleer C. Seetharaman K. et al . (2011). Optimizing the performances of a p300-based brain-computer interface in ambulatory conditions. IEEE J. Emerg. Select. Top. Circ. Syst. 1, 566–577. 10.1109/JETCAS.2011.2179421

  • CrossRef
  • Google Scholar

• 24

  Centers for Medicare & Medicaid Services. (2023). CMS.gov. Available online at: https://www.cms.gov/ (accessed February 14, 2023).

  • Google Scholar

• 25

  Chenausky K. V. Tager-Flusberg H. (2022). The importance of deep speech phenotyping for neurodevelopmental and genetic disorders: a conceptual review. J. Neurodev. Disord. 14, 1–14. 10.1186/s11689-022-09443-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  Clark D. J. Manini T. M. Ferris D. P. Hass C. J. Brumback B. A. Cruz-Almeida Y. et al . (2020). Multimodal imaging of brain activity to investigate walking and mobility decline in older adults (mind in motion study): hypothesis, theory, and methods. Front. Aging Neurosci. 11, 358. 10.3389/fnagi.2019.00358

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27

  Cortney Bradford J. Lukos J. R. Passaro A. Ries A. Ferris D. P. (2019). Effect of locomotor demands on cognitive processing. Sci. Rep. 9, 1–12. 10.1038/s41598-019-45396-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28

  Dai Y. Wang J. Gao S. (2022). Advanced electronics and artificial intelligence: must-have technologies toward human body digital twins. Adv. Intell. Syst. 2022, 2100263. 10.1007/978-3-031-14054-9

  • CrossRef
  • Google Scholar

• 29

  De Bartolo D. Morone G. Giordani G. Antonucci G. Russo V. Fusco A. et al . (2020). Effect of different music genres on gait patterns in parkinson's disease. Neurol. Sci. 41, 575–582. 10.1007/s10072-019-04127-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30

  De Sanctis P. Butler J. S. Malcolm B. R. Foxe J. J. (2014). Recalibration of inhibitory control systems during walking-related dual-task interference: a mobile brain-body imaging (mobi) study. Neuroimage94, 55–64. 10.1016/j.neuroimage.2014.03.016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31

  De Sanctis P. Wagner J. Molholm S. Foxe J. J. Blumen H. M. Horsthuis D. J. (2023). Neural signature of mobility-related everyday function in older adults at-risk of cognitive impairment. Neurobiol. Aging122, 1–11. 10.1016/j.neurobiolaging.2022.11.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32

  de Tommaso M. Vecchio E. Ricci K. Montemurno A. De Venuto D. Annese V. F. (2015). Combined EEG/EMG evaluation during a novel dual task paradigm for gait analysis, in 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI) (Gallipoli: IEEE), 181–186.

  • Google Scholar

• 33

  Divya R. Peter J. D. (2022). Smart healthcare system-a brain-like computing approach for analyzing the performance of detectron2 and posenet models for anomalous action detection in aged people with movement impairments. Complex Intell. Syst. 8, 3021–3040. 10.1007/s40747-021-00319-8

  • CrossRef
  • Google Scholar

• 34

  do Nascimento O. F. Nielsen K. D. Voigt M. (2005). Influence of directional orientations during gait initiation and stepping on movement-related cortical potentials. Behav. Brain Res. 161, 141–154. 10.1016/j.bbr.2005.02.031

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35

  Doubeni C. A. Simon M. Krist A. H. (2021). Addressing systemic racism through clinical preventive service recommendations from the us preventive services task force. JAMA325, 627–628. 10.1001/jama.2020.26188

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36

  Dror I. E. (2020). Cognitive and human factors in expert decision making: six fallacies and the eight sources of bias. Anal. Chem. 92, 7998–8004. 10.1021/acs.analchem.0c00704

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37

  Eggenberger P. Wolf M. Schumann M. De Bruin E. D. (2016). Exergame and balance training modulate prefrontal brain activity during walking and enhance executive function in older adults. Front. Aging Neurosci. 8, 66. 10.3389/fnagi.2016.00066

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38

  Enders H. Cortese F. Maurer C. Baltich J. Protzner A. B. Nigg B. M. (2016). Changes in cortical activity measured with eeg during a high-intensity cycling exercise. J. Neurophysiol. 115, 379–388. 10.1152/jn.00497.2015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39

  Figueiredo J. Santos C. P. Moreno J. C. (2018). Automatic recognition of gait patterns in human motor disorders using machine learning: a review. Med. Eng. Phys. 53, 1–12. 10.1016/j.medengphy.2017.12.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 40

  Fontes E. B. Bortolotti H. da Costa K. G. de Campos B. M. Castanho G. K. Hohl R. et al . (2020). Modulation of cortical and subcortical brain areas at low and high exercise intensities. Br. J. Sports Med. 54, 110–115. 10.1136/bjsports-2018-100295

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41

  Fraser S. A. Dupuy O. Pouliot P. Lesage F. Bherer L. (2016). Comparable cerebral oxygenation patterns in younger and older adults during dual-task walking with increasing load. Front. Aging Neurosci. 8, 240. 10.3389/fnagi.2016.00240

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42

  Fumoto M. Oshima T. Kamiya K. Kikuchi H. Seki Y. Nakatani Y. et al . (2010). Ventral prefrontal cortex and serotonergic system activation during pedaling exercise induces negative mood improvement and increased alpha band in eeg. Behav. Brain Res. 213, 1–9. 10.1016/j.bbr.2010.04.017

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43

  GE HealthCare. (2023). ©General Electric HealthCare. Available online at: https://www.gehealthcare.com/ (accessed February 14, 2023).

  • Google Scholar

• 44

  Gershon R. C. Cella D. Fox N. A. Havlik R. J. Hendrie H. C. Wagster M. V. (2010). Assessment of neurological and behavioural function: the nih toolbox. Lancet Neurol. 9, 138–139. 10.1016/S1474-4422(09)70335-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  Ghai S. Ghai I. Schmitz G. Effenberg A. O. (2018). Effect of rhythmic auditory cueing on parkinsonian gait: a systematic review and meta-analysis. Sci. Rep. 8, 1–19. 10.1038/s41598-017-16232-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 46

  Giles G. E. Cantelon J. A. Eddy M. D. Brunyé T. T. Urry H. L. Taylor H. A. et al . (2018). Cognitive reappraisal reduces perceived exertion during endurance exercise. Motiv. Emot. 42, 482–496. 10.1007/s11031-018-9697-z

  • CrossRef
  • Google Scholar

• 47

  Gramann K. Gwin J. T. Bigdely-Shamlo N. Ferris D. P. Makeig S. (2010). Visual evoked responses during standing and walking. Front. Hum. Neurosci. 4, 202. 10.3389/fnhum.2010.00202

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 48

  Guayacán L. C. Martínez F. (2021). Visualising and quantifying relevant parkinsonian gait patterns using 3d convolutional network. J. Biomed. Inform. 123, 103935. 10.1016/j.jbi.2021.103935

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49

  Gwin J. T. Gramann K. Makeig S. Ferris D. P. (2010). Removal of movement artifact from high-density EEG recorded during walking and running. J. Neurophysiol. 103, 3526–3534. 10.1152/jn.00105.2010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50

  Gwin J. T. Gramann K. Makeig S. Ferris D. P. (2011). Electrocortical activity is coupled to gait cycle phase during treadmill walking. Neuroimage54, 1289–1296. 10.1016/j.neuroimage.2010.08.066

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51

  Hallett M. DelRosso L. M. Elble R. Ferri R. Horak F. B. Lehericy S. et al . (2021). Evaluation of movement and brain activity. Clin. Neurophysiol. 132, 2608–2638. 10.1016/j.clinph.2021.04.023

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52

  Harada T. Miyai I. Suzuki M. Kubota K. (2009). Gait capacity affects cortical activation patterns related to speed control in the elderly. Exp. Brain Res. 193, 445–454. 10.1007/s00221-008-1643-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  Hasan S. S. Siddiquee M. R. Bai O. (2019). Supervised classification of eeg signals with score threshold regulation for pseudo-online asynchronous detection of gait intention, in 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA) (Boca Raton, F: IEEE), 1476–1479.

  • Google Scholar

• 54

  Hatami T. Hamghalam M. Reyhani-Galangashi O. Mirzakuchaki S. (2019). A machine learning approach to brain tumors segmentation using adaptive random forest algorithm, in 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI) (Tehran: IEEE), 076–082.

  • Pubmed Abstract
  • Google Scholar

• 55

  Hausmann S. B. Vargas A. M. Mathis A. Mathis M. W. (2021). Measuring and modeling the motor system with machine learning. Curr. Opin. Neurobiol. 70, 11–23. 10.1016/j.conb.2021.04.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 56

  Holtzer R. Mahoney J. R. Izzetoglu M. Izzetoglu K. Onaral B. Verghese J. (2011). fnirs study of walking and walking while talking in young and old individuals. J. Gerontol. A Biomed. Sci. Med. Sci. 66, 879–887. 10.1093/gerona/glr068

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 57

  Hornby T. G. Moore J. L. Lovell L. Roth E. J. (2016). Influence of skill and exercise training parameters on locomotor recovery during stroke rehabilitation. Curr. Opin. Neurol. 29, 677. 10.1097/WCO.0000000000000397

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 58

  Hülser M. Spielmann H. Oertel J. Sippl C. (2022). Motor skills, cognitive impairment, and quality of life in normal pressure hydrocephalus: early effects of shunt placement. Acta Neurochir. 164, 1765–1775. 10.1007/s00701-022-05149-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59

  Ikeda T. Matsushita A. Saotome K. Hasegawa Y. Matsumura A. Sankai Y. (2016). Muscle activity during gait-like motion provided by MRI compatible lower-extremity motion simulator. Adv. Robot. 30, 459–475. 10.1080/01691864.2015.1122552

  • CrossRef
  • Google Scholar

• 60

  Iseki K. Hanakawa T. Shinozaki J. Nankaku M. Fukuyama H. (2008). Neural mechanisms involved in mental imagery and observation of gait. Neuroimage41, 1021–1031. 10.1016/j.neuroimage.2008.03.010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 61

  Jahn K. Deutschländer A. Stephan T. Strupp M. Wiesmann M. Brandt T. (2004). Brain activation patterns during imagined stance and locomotion in functional magnetic resonance imaging. Neuroimage22, 1722–1731. 10.1016/j.neuroimage.2004.05.017

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 62

  Jain S. Gourab K. Schindler-Ivens S. Schmit B. D. (2013). EEG during pedaling: evidence for cortical control of locomotor tasks. Clin. Neurophysiol. 124, 379–390. 10.1016/j.clinph.2012.08.021

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63

  Kameli N. Dragojlovic-Kerkache A. Savelkoul P. Stassen F. R. (2021). Plant-derived extracellular vesicles: current findings, challenges, and future applications. Membranes11, 411. 10.3390/membranes11060411

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 64

  Kashuba V. Stepanenko O. Byshevets N. Kharchuk O. Savliuk S. Bukhovets B. et al . (2020). The formation of human movement and sports skills in processing sports-pedagogical and biomedical data in masters of sports. Int. J. Human Movement Sport Sci.8, 249–257. 10.13189/saj.2020.080513

  • CrossRef
  • Google Scholar

• 65

  Keramidas M. E. Kounalakis S. N. Eiken O. Mekjavic I. B. (2011). Muscle and cerebral oxygenation during exercise performance after short-term respiratory work. Respiratory Physiol. Neurobiol. 175, 247–254. 10.1016/j.resp.2010.11.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 66

  Kerkman J. N. Daffertshofer A. Gollo L. L. Breakspear M. Boonstra T. W. (2018). Network structure of the human musculoskeletal system shapes neural interactions on multiple time scales. Sci. Adv. 4, eaat0497. 10.1126/sciadv.aat0497

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 67

  Khaksar S. Pan H. Borazjani B. Murray I. Agrawal H. Liu W. et al . (2021). Application of inertial measurement units and machine learning classification in cerebral palsy: randomized controlled trial. JMIR Rehabil. Assist. Technol. 8, e29769. 10.2196/29769

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 68

  Khan R. A. Naseer N. Qureshi N. K. Noori F. M. Nazeer H. Khan M. U. (2018). fnirs-based neurorobotic interface for gait rehabilitation. J. Neuroeng. Rehabil. 15, 1–17. 10.1186/s12984-018-0346-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69

  Klibaite U. Kislin M. Verpeut J. L. Bergeler S. Sun X. Shaevitz J. W. et al . (2022). Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models. Mol. Autism13, 1–18. 10.1186/s13229-022-00492-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70

  Kline A. Pittman D. Ronsky J. Goodyear B. (2020). Differentiating the brain's involvement in executed and imagined stepping using fmri. Behav. Brain Res. 394, 112829. 10.1016/j.bbr.2020.112829

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 71

  Kumar N. Michmizos K. P. (2022). A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity. Sci. Rep. 12, 1–12. 10.1038/s41598-022-05079-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72

  Kurz M. J. Wilson T. W. Arpin D. J. (2012). Stride-time variability and sensorimotor cortical activation during walking. Neuroimage59, 1602–1607. 10.1016/j.neuroimage.2011.08.084

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 73

  Labriffe M. Annweiler C. Amirova L. E. Gauquelin-Koch G. Ter Minassian A. Leiber L.-M. et al . (2017). Brain activity during mental imagery of gait versus gait-like plantar stimulation: a novel combined functional mri paradigm to better understand cerebral gait control. Front. Hum. Neurosci. 11, 106. 10.3389/fnhum.2017.00106

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74

  Lacerenza M. Spinelli L. Buttafava M. Dalla Mora A. Zappa F. Pifferi A. et al . (2021). Monitoring the motor cortex hemodynamic response function in freely moving walking subjects: a time-domain fnirs pilot study. Neurophotonics8, 015006. 10.1117/1.NPh.8.1.015006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75

  Ladouce S. Donaldson D. I. Dudchenko P. A. Ietswaart M. (2019). Mobile eeg identifies the re-allocation of attention during real-world activity. Sci. Rep. 9, 1–10. 10.1038/s41598-019-51996-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76

  Lau T. M. Gwin J. T. Ferris D. P. (2014). Walking reduces sensorimotor network connectivity compared to standing. J. Neuroeng. Rehabil. 11, 1–10. 10.1186/1743-0003-11-14

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 77

  Lau T. M. Gwin J. T. McDowell K. G. Ferris D. P. (2012). Weighted phase lag index stability as an artifact resistant measure to detect cognitive eeg activity during locomotion. J. Neuroeng. Rehabil. 9, 1–9. 10.1186/1743-0003-9-47

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 78

  Lee T.-W. Girolami M. Sejnowski T. J. (1999). Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput. 11, 417–441. 10.1162/089976699300016719

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79

  Lewis R. Gómez Álvarez C. B. Rayman M. Lanham-New S. Woolf A. Mobasheri A. (2019). Strategies for optimising musculoskeletal health in the 21st century. BMC Musculoskeletal Disord. 20, 1–15. 10.1186/s12891-019-2510-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80

  Li C. Su M. Xu J. Jin H. Sun L. (2020). A between-subject fnirs-bci study on detecting self-regulated intention during walking. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 531–540. 10.1109/TNSRE.2020.2965628

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81

  Li J. Chen G. Thangavel P. Yu H. Thakor N. Bezerianos A. et al . (2016). A robotic knee exoskeleton for walking assistance and connectivity topology exploration in eeg signal, in 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob) (Singapore: IEEE), 1068–1073.

  • Google Scholar

• 82

  Li J. Dimitrakopoulos G. N. Thangavel P. Chen G. Sun Y. Guo Z. et al . (2019). What are spectral and spatial distributions of eeg-emg correlations in overground walking? an exploratory study. IEEE Access7, 143935–143946. 10.1109/ACCESS.2019.2945602

  • CrossRef
  • Google Scholar

• 83

  Li R. Wang X. Lawler K. Garg S. Bai Q. Alty J. (2022a). Applications of artificial intelligence to aid detection of dementia: a scoping review on current capabilities and future directions. J. Biomed. Inform. 2022, 104030. 10.1016/j.jbi.2022.104030

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84

  Li R. Yang D. Fang F. Hong K.-S. Reiss A. L. Zhang Y. (2022b). Concurrent FNIRS and EEG for brain function investigation: a systematic, methodology-focused review. Sensors22, 5865. 10.3390/s22155865

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 85

  Lin Y.-P. Wang Y. Jung T.-P. (2014). Assessing the feasibility of online ssvep decoding in human walking using a consumer eeg headset. J. Neuroeng. Rehabil. 11, 1–8. 10.1186/1743-0003-11-119

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 86

  Lu C.-F. Liu Y.-C. Yang Y.-R. Wu Y.-T. Wang R.-Y. (2015). Maintaining gait performance by cortical activation during dual-task interference: a functional near-infrared spectroscopy study. PLoS ONE10, e0129390. 10.1145/2818302

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87

  Ludyga S. Gronwald T. Hottenrott K. (2016). Effects of high vs. low cadence training on cyclists' brain cortical activity during exercise. J. Sci. Med. Sport19, 342–347. 10.1016/j.jsams.2015.04.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88

  Luu T. P. Brantley J. A. Zhu F. Contreras-Vidal J. L. (2017a). Cortical features of locomotion-mode transitions via non-invasive EEG, in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (Banff, AB: IEEE), 2437–2441.

  • Google Scholar

• 89

  Luu T. P. Nakagome S. He Y. Contreras-Vidal J. L. (2017b). Real-time eeg-based brain-computer interface to a virtual avatar enhances cortical involvement in human treadmill walking. Sci. Rep. 7, 1–12. 10.1038/s41598-017-09187-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90

  Magrinelli F. Balint B. Bhatia K. P. (2021). Challenges in clinicogenetic correlations: one gene-many phenotypes. Mov. Disord. Clin. Pract. 8, 299–310. 10.1002/mdc3.13165

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91

  Mahlknecht P. Kiechl S. Bloem B. R. Willeit J. Scherfler C. Gasperi A. et al . (2013). Prevalence and burden of gait disorders in elderly men and women aged 60-97 years: a population-based study. PLoS ONE8, e69627. 10.1371/journal.pone.0069627

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92

  Makeig S. Gramann K. Jung T.-P. Sejnowski T. J. Poizner H. (2009). Linking brain, mind and behavior. Int. J. Psychophysiol. 73, 95–100. 10.1016/j.ijpsycho.2008.11.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93

  Makizako H. Shimada H. Park H. Tsutsumimoto K. Uemura K. Suzuki T. et al . (2013). Brain activation during dual-task walking and executive function among older adults with mild cognitive impairment: a fnirs study. Aging Clin. Exp. Res. 25, 539–544. 10.1007/s40520-013-0119-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94

  Malcolm B. R. Foxe J. J. Butler J. S. De Sanctis P. (2015). The aging brain shows less flexible reallocation of cognitive resources during dual-task walking: a mobile brain/body imaging (mobi) study. Neuroimage117, 230–242. 10.1016/j.neuroimage.2015.05.028

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95

  Malouin F. Richards C. L. Jackson P. L. Dumas F. Doyon J. (2003). Brain activations during motor imagery of locomotor-related tasks: a pet study. Hum. Brain Mapp. 19, 47–62. 10.1002/hbm.10103

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96

  Martini M. L. Oermann E. K. Opie N. L. Panov F. Oxley T. Yaeger K. (2020). Sensor modalities for brain-computer interface technology: a comprehensive literature review. Neurosurgery86, E108-E117. 10.1093/neuros/nyz286

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97

  Mazurek K. A. Patelaki E. Foxe J. J. Freedman E. G. (2021). Using the mobi motion capture system to rapidly and accurately localize EEG electrodes in anatomic space. Eur. J. Neurosci. 54, 8396–8405. 10.1111/ejn.15019

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98

  Meester D. Al-Yahya E. Dawes H. Martin-Fagg P. Piñon C. (2014). Associations between prefrontal cortex activation and h-reflex modulation during dual task gait. Front. Hum. Neurosci. 8, 78. 10.3389/fnhum.2014.00078

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99

  Mehra D. Tiwari A. Joshi D. (2021). Investigating neural correlates of locomotion transition via temporal relation of eeg and eog-recorded eye movements. Comput. Biol. Med. 132, 104350. 10.1016/j.compbiomed.2021.104350

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100

  Minetto M. A. Giannini A. McConnell R. Busso C. Torre G. Massazza G. (2020). Common musculoskeletal disorders in the elderly: the star triad. J. Clin. Med. 9, 1216. 10.3390/jcm9041216

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 101

  Mirelman A. Maidan I. Bernad-Elazari H. Nieuwhof F. Reelick M. Giladi N. et al . (2014). Increased frontal brain activation during walking while dual tasking: an fnirs study in healthy young adults. J. Neuroeng. Rehabil. 11, 1–7. 10.1186/1743-0003-11-85

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102

  Mirelman A. Maidan I. Bernad-Elazari H. Shustack S. Giladi N. Hausdorff J. M. (2017). Effects of aging on prefrontal brain activation during challenging walking conditions. Brain Cogn. 115, 41–46. 10.1016/j.bandc.2017.04.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 103

  Miyai I. Tanabe H. C. Sase I. Eda H. Oda I. Konishi I. et al . (2001). Cortical mapping of gait in humans: a near-infrared spectroscopic topography study. Neuroimage14, 1186–1192. 10.1006/nimg.2001.0905

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 104

  Muñoz-Lasa S. de Silanes C. L. Atín-Arratibel M. Á Bravo-LlatasC.Pastor-JimenoS.et al. (2019). Effects of hippotherapy in multiple sclerosis: pilot study on quality of life, spasticity, gait, pelvic floor, depression and fatigue. Med. Clin. (English Edition) 152, 55–58. 10.1016/j.medcle.2018.11.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 105

  Nakagome S. Luu T. P. Brantley J. A. Contreras-Vidal J. L. (2017). Prediction of emg envelopes of multiple terrains over-ground walking from eeg signals using an unscented kalman filter, in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (Banff, AB: IEEE), 3175–3178.

  • Google Scholar

• 106

  NIRx. (2023). ©NIRx Medical Technologies. Available online at: https://nirx.net/ (accessed February 14, 2023).

  • Google Scholar

• 107

  NordicNeuroLab. (2023). ©NordicNeuroLab. Available online at: https://www.nordicneurolab.com/ (accessed February 14, 2023).

  • Google Scholar

• 108

  Nojiri K. Iwane F. (2014). Motion direction estimation of walking base on eeg signal, in 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Besacon: IEEE), 542–547.

  • Google Scholar

• 109

  Norbye A. D. Midgard R. Thrane G. (2020). Spasticity, gait, and balance in patients with multiple sclerosis: a cross-sectional study. Physiother. Res. Int. 25, e1799. 10.1002/pri.1799

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 110

  Nordin A. D. Hairston W. D. Ferris D. P. (2018). Dual-electrode motion artifact cancellation for mobile electroencephalography. J. Neural Eng. 15, 056024. 10.1088/1741-2552/aad7d7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111

  Nordin A. D. Hairston W. D. Ferris D. P. (2019a). Faster gait speeds reduce alpha and beta eeg spectral power from human sensorimotor cortex. IEEE Trans. Biomed. Eng. 67, 842–853. 10.1109/TBME.2019.2921766

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 112

  Nordin A. D. Hairston W. D. Ferris D. P. (2019b). Human electrocortical dynamics while stepping over obstacles. Sci. Rep. 9, 1–12. 10.1038/s41598-019-41131-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113

  Oliveira A. S. Schlink B. R. Hairston W. D. König P. Ferris D. P. (2016). Induction and separation of motion artifacts in EEG data using a mobile phantom head device. J. Neural Eng. 13, 036014. 10.1088/1741-2560/13/3/036014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 114

  Palmer J. A. Kreutz-Delgado K. Makeig S. (2006). Super-gaussian mixture source model for ICA, in International Conference on Independent Component Analysis and Signal Separation. ICA 2006, eds RoscaJ.ErdogmusD.PríncipeJ. C.HaykinS. (Berlin; Heidelberg: Springer). 10.1007/11679363_106

  • CrossRef
  • Google Scholar

• 115

  Parmentier T. Monteith G. Cortez M. A. Wielaender F. Fischer A. Jokinen T. S. et al . (2020). Effect of prior general anesthesia or sedation and antiseizure drugs on the diagnostic utility of wireless video electroencephalography in dogs. J. Vet. Internal Med. 34, 1967–1974. 10.1111/jvim.15856

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 116

  Patil P. Kumar K. S. Gaud N. Semwal V. B. (2019). Clinical human gait classification: extreme learning machine approach, in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) (Dhaka: IEEE), 1–6.

  • Google Scholar

• 117

  Pearson-Stuttard J. Ezzati M. Gregg E. W. (2019). Multimorbidity–a defining challenge for health systems. Lancet Public Health4, e599-e600. 10.1016/S2468-2667(19)30222-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 118

  Peters S. Lim S. B. Louie D. R. Yang C.-L. Eng J. J. (2020). Passive, yet not inactive: robotic exoskeleton walking increases cortical activation dependent on task. J. Neuroeng. Rehabil. 17, 1–12. 10.1186/s12984-020-00739-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 119

  Petersen T. H. Willerslev-Olsen M. Conway B. A. Nielsen J. B. (2012). The motor cortex drives the muscles during walking in human subjects. J. Physiol. 590, 2443–2452. 10.1113/jphysiol.2012.227397

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 120

  Pires F. O. Dos Anjos C. A. Covolan R. J. Pinheiro F. A. St Clair Gibson A. Noakes T. D. et al . (2016). Cerebral regulation in different maximal aerobic exercise modes. Front. Physiol. 7, 253. 10.3389/fphys.2016.00253

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 121

  Pizzamiglio S. Naeem U. Abdalla H. Turner D. L. (2017). Neural correlates of single-and dual-task walking in the real world. Front. Hum. Neurosci. 11, 460. 10.3389/fnhum.2017.00460

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 122

  Pons J. L. Moreno J. C. Torricelli D. Taylor J. (2013). Principles of human locomotion: a review, in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Dhaka: IEEE), 6941–6944.

  • Pubmed Abstract
  • Google Scholar

• 123

  Porrselvi A. P. (2022). Tam battery: development and pilot testing of a tamil computer-assisted cognitive test battery for older adults. Clin. Neuropsychol. 15, 1–20. 10.1080/13854046.2022.2156396

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 124

  Presacco A. Forrester L. W. Contreras-Vidal J. L. (2012). Decoding intra-limb and inter-limb kinematics during treadmill walking from scalp electroencephalographic (EEG) signals. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 212–219. 10.1109/TNSRE.2012.2188304

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 125

  Presacco A. Goodman R. Forrester L. Contreras-Vidal J. L. (2011). Neural decoding of treadmill walking from noninvasive electroencephalographic signals. J. Neurophysiol. 106, 1875–1887. 10.1152/jn.00104.2011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 126

  Quiroz G. Espinoza-Valdez A. Salido-Ruiz R. Mercado L. (2017). Coherence analysis of eeg in locomotion using graphs. Revista mexicana de ingeniería biomédica38, 235–246.

  • Google Scholar

• 127

  Racinais S. Buchheit M. Girard O. (2014). Breakpoints in ventilation, cerebral and muscle oxygenation, and muscle activity during an incremental cycling exercise. Front. Physiol. 5, 142. 10.3389/fphys.2014.00142

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 128

  Radel R. Brisswalter J. Perrey S. (2017). Saving mental effort to maintain physical effort: a shift of activity within the prefrontal cortex in anticipation of prolonged exercise. Cognit. Affect. Behav. Neurosci. 17, 305–314. 10.3758/s13415-016-0480-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 129

  Richer N. Downey R. J. Hairston W. D. Ferris D. P. Nordin A. D. (2020). Motion and muscle artifact removal validation using an electrical head phantom, robotic motion platform, and dual layer mobile eeg. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 1825–1835. 10.1109/TNSRE.2020.3000971

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 130

  Runge M. Hunter G. (2006). Determinants of musculoskeletal frailty and the risk of falls in old age. J. Musculoskeletal Neuronal Interact. 6, 167.

  • Pubmed Abstract
  • Google Scholar

• 131

  Sacco K. Cauda F. Cerliani L. Mate D. Duca S. Geminiani G. C. (2006). Motor imagery of walking following training in locomotor attention. The effect of ‘the tango lesson'. Neuroimage32, 1441–1449. 10.1016/j.neuroimage.2006.05.018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 132

  Sahyoun C. Floyer-Lea A. Johansen-Berg H. Matthews P. M. (2004). Towards an understanding of gait control: brain activation during the anticipation, preparation and execution of foot movements. Neuroimage21, 568–575. 10.1016/j.neuroimage.2003.09.065

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133

  Salazar-Varas R. Costa Á. Iáñez E. Úbeda A. Hortal E. Azorín J. (2015). Analyzing eeg signals to detect unexpected obstacles during walking. J. Neuroeng. Rehabil. 12, 1–15. 10.1186/s12984-015-0095-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 134

  Schneider S. Rouffet D. Billaut F. Strüder H. (2013). Cortical current density oscillations in the motor cortex are correlated with muscular activity during pedaling exercise. Neuroscience228, 309–314. 10.1016/j.neuroscience.2012.10.037

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 135

  Seeber M. Scherer R. Wagner J. Solis-Escalante T. Müller-Putz G. R. (2014). Eeg beta suppression and low gamma modulation are different elements of human upright walking. Front. Hum. Neurosci. 8, 485. 10.3389/fnhum.2014.00485

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 136

  Seeber M. Scherer R. Wagner J. Solis-Escalante T. Müller-Putz G. R. (2015). High and low gamma eeg oscillations in central sensorimotor areas are conversely modulated during the human gait cycle. Neuroimage112, 318–326. 10.1016/j.neuroimage.2015.03.045

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 137

  Sejdić E. Godfrey A. McIlroy W. Montero-Odasso M. (2019). Engineering human gait and the potential role of wearable, in Falls and Cognition in Older Persons, eds Montero-OdassoM.CamicioliR. (Cham: Springer). 10.1007/978-3-030-24233-6_22

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 138

  Severens M. Nienhuis B. Desain P. Duysens J. (2012). Feasibility of measuring event related desynchronization with electroencephalography during walking, in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (San Diego, CA: IEEE), 2764–2767.

  • Pubmed Abstract
  • Google Scholar

• 139

  Severens M. Perusquia-Hernandez M. Nienhuis B. Farquhar J. Duysens J. (2014). Using actual and imagined walking related desynchronization features in a bci. IEEE Trans. Neural Syst. Rehabil. Eng. 23, 877–886. 10.1109/TNSRE.2014.2371391

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 140

  Shibuya K. Tanaka J. Kuboyama N. Muraj S. Ogaki T. (2004). supramaximal exhaustive exercise. J. Sports Med. Phys. Fitness44, 2.

  • Google Scholar

• 141

  Sipp A. R. Gwin J. T. Makeig S. Ferris D. P. (2013). Loss of balance during balance beam walking elicits a multifocal theta band electrocortical response. J. Neurophysiol. 110, 2050–2060. 10.1152/jn.00744.2012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 142

  Sisters of Charity of Leavenworth Health Services Corporation. (2023). ©SCL Health Medical Group. Available online at: https://www.sclhealth.org/locations/medical-group/ (accessed February 14, 2023).

  • Google Scholar

• 143

  Skinner J. W. Lee H. K. Hass C. J. (2022). Evaluation of gait termination strategy in individuals with essential tremor and parkinson's disease. Gait Posture92, 338–342. 10.1016/j.gaitpost.2021.12.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 144

  Smania N. Bonetti P. Gandolfi M. Cosentino A. Waldner A. Hesse S. et al . (2011). Improved gait after repetitive locomotor training in children with cerebral palsy. Am. J. Phys. Med. Rehabil. 90, 137–149. 10.1097/PHM.0b013e318201741e

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 145

  Smith K. J. Billaut F. (2010). Influence of cerebral and muscle oxygenation on repeated-sprint ability. Eur. J. Appl. Physiol. 109, 989–999. 10.1007/s00421-010-1444-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 146

  Snyder K. L. Kline J. E. Huang H. J. Ferris D. P. (2015). Independent component analysis of gait-related movement artifact recorded using EEG electrodes during treadmill walking. Front. Hum. Neurosci. 9, 639. 10.3389/fnhum.2015.00639

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 147

  Song S. Kidziński Ł. Peng X. B. Ong C. Hicks J. Levine S. et al . (2021). Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation. J. Neuroeng. Rehabil. 18, 1–17. 10.1186/s12984-021-00919-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 148

  Stolbkov Y. Moshonkina T. Orlov I. Tomilovskaya E. Kozlovskaya I. Gerasimenko Y. P. (2019). The neurophysiological correlates of real and imaginary locomotion. Hum. Physiol. 45, 104–114. 10.1134/S0362119719010146

  • CrossRef
  • Google Scholar

• 149

  Storzer L. Butz M. Hirschmann J. Abbasi O. Gratkowski M. Saupe D. et al . (2016). Bicycling and walking are associated with different cortical oscillatory dynamics. Front. Hum. Neurosci. 10, 61. 10.3389/fnhum.2016.00061

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 150

  Stratus. (2023). ©Stratus. Available online at: https://stratusneuro.com/clinical-trials/ (accessed February 14, 2023).

  • Google Scholar

• 151

  Subudhi A. W. Dimmen A. C. Roach R. C. (2007). Effects of acute hypoxia on cerebral and muscle oxygenation during incremental exercise. J. Appl. Physiol. 103, 177–183. 10.1152/japplphysiol.01460.2006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 152

  Subudhi A. W. Miramon B. R. Granger M. E. Roach R. C. (2009). Frontal and motor cortex oxygenation during maximal exercise in normoxia and hypoxia. J. Appl. Physiol. 106, 1153–1158. 10.1152/japplphysiol.91475.2008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 153

  Suzuki M. Miyai I. Ono T. Kubota K. (2008). Activities in the frontal cortex and gait performance are modulated by preparation. An fnirs study. Neuroimage39, 600–607. 10.1016/j.neuroimage.2007.08.044

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 154

  Suzuki M. Miyai I. Ono T. Oda I. Konishi I. Kochiyama T. et al . (2004). Prefrontal and premotor cortices are involved in adapting walking and running speed on the treadmill: an optical imaging study. Neuroimage23, 1020–1026. 10.1016/j.neuroimage.2004.07.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 155

  Symeonidou E.-R. Nordin A. D. Hairston W. D. Ferris D. P. (2018). Effects of cable sway, electrode surface area, and electrode mass on electroencephalography signal quality during motion. Sensors18, 1073. 10.3390/s18041073

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 156

  Takahiro I. Akira M. Kousaku S. Yasuhisa H. Yoshiyuki S. (2013). Preliminary report of brain activities during active and passive gait-like motion, in 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) (San Diego, CA: IEEE), 1566–1569

  • Google Scholar

• 157

  Talamonti D. Vincent T. Fraser S. Nigam A. Lesage F. Bherer L. (2021). The benefits of physical activity in individuals with cardiovascular risk factors: a longitudinal investigation using fnirs and dual-task walking. J. Clin. Med. 10, 579. 10.3390/jcm10040579

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 158

  Talo M. Yildirim O. Baloglu U. B. Aydin G. Acharya U. R. (2019). Convolutional neural networks for multi-class brain disease detection using mri images. Comput. Med. Imaging Graphics78, 101673. 10.1016/j.compmedimag.2019.101673

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 159

  Thomas R. Stephane P. (2008). Prefrontal cortex oxygenation and neuromuscular responses to exhaustive exercise. Eur. J. Appl. Physiol. 102, 153–163. 10.1007/s00421-007-0568-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 160

  Tortora S. Ghidoni S. Chisari C. Micera S. Artoni F. (2020). Deep learning-based bci for gait decoding from eeg with lstm recurrent neural network. J. Neural Eng. 17, 046011. 10.1088/1741-2552/ab9842

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 161

  Úbeda A. Planelles D. Costa A. Hortal E. Iáñez E. Azorín J. M. (2014). Decoding knee angles from EEG signals for different walking speeds, in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (San Diego, CA: IEEE), 1475–1478.

  • Google Scholar

• 162

  Vásquez-Correa J. C. Arias-Vergara T. Orozco-Arroyave J. R. Eskofier B. Klucken J. Nöth E. (2018). Multimodal assessment of parkinson's disease: a deep learning approach. IEEE J. Biomed. Health Inform. 23, 1618–1630. 10.1109/JBHI.2018.2866873

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 163

  Velu P. D. de Sa V. R. (2013). Single-trial classification of gait and point movement preparation from human EEG. Front. Neurosci. 7, 84. 10.3389/fnins.2013.00084

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 164

  Vitorio R. Stuart S. Gobbi L. T. Rochester L. Alcock L. Pantall A. (2018). Reduced gait variability and enhanced brain activity in older adults with auditory cues: a functional near-infrared spectroscopy study. Neurorehabil. Neural Repair. 32, 976–987. 10.1177/1545968318805159

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 165

  Vrana S. R. Vrana D. T. (2017). Can a computer administer a wechsler intelligence test?Profess. Psychol. Res. Pract. 48, 191. 10.1037/pro0000128

  • CrossRef
  • Google Scholar

• 166

  Wagner J. Makeig S. Gola M. Neuper C. Müller-Putz G. (2016). Distinct β band oscillatory networks subserving motor and cognitive control during gait adaptation. J. Neurosci. 36, 2212–2226. 10.1523/JNEUROSCI.3543-15.2016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 167

  Wagner J. Martinez-Cancino R. Delorme A. Makeig S. Solis-Escalante T. Neuper C. et al . (2019a). High-density eeg mobile brain/body imaging data recorded during a challenging auditory gait pacing task. Scientific Data6, 1–9. 10.1038/s41597-019-0223-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 168

  Wagner J. Martínez-Cancino R. Makeig S. (2019b). Trial-by-trial source-resolved eeg responses to gait task challenges predict subsequent step adaptation. Neuroimage199, 691–703. 10.1016/j.neuroimage.2019.06.018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 169

  Wagner J. Solis-Escalante T. Grieshofer P. Neuper C. Müller-Putz G. Scherer R. (2012). Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor eeg rhythms in able-bodied subjects. Neuroimage63, 1203–1211. 10.1016/j.neuroimage.2012.08.019

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 170

  Wagner J. Solis-Escalante T. Scherer R. Neuper C. Müller-Putz G. (2014). It's how you get there: walking down a virtual alley activates premotor and parietal areas. Front. Hum. Neurosci. 8, 93. 10.3389/fnhum.2014.00093

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 171

  Wang J. Wai Y. Weng Y. Ng K. Huang Y.-Z. Ying L. et al . (2009). Functional mri in the assessment of cortical activation during gait-related imaginary tasks. J. Neural Transm. 116, 1087–1092. 10.1007/s00702-009-0269-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 172

  Warmerdam E. Hausdorff J. M. Atrsaei A. Zhou Y. Mirelman A. Aminian K. et al . (2020). Long-term unsupervised mobility assessment in movement disorders. Lancet Neurol. 19, 462–470. 10.1016/S1474-4422(19)30397-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 173

  Weersink J. B. Maurits N. M. de Jong B. M. (2019). EEG time-frequency analysis provides arguments for arm swing support in human gait control. Gait Posture70, 71–78. 10.1016/j.gaitpost.2019.02.017

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 174

  Wei P. Zhang J. Wang B. Hong J. (2021). Surface electromyography and electroencephalogram-based gait phase recognition and correlations between cortical and locomotor muscle in the seven gait phases. Front. Neurosci. 15, 607905. 10.3389/fnins.2021.607905

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 175

  Wieser M. Haefeli J. Bütler L. Jäncke L. Riener R. Koeneke S. (2010). Temporal and spatial patterns of cortical activation during assisted lower limb movement. Exp. Brain Res. 203, 181–191. 10.1007/s00221-010-2223-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 176

  Xiang K. Liu Y. Sun L. (2021). Motoric cognitive risk syndrome: symptoms, pathology, diagnosis, and recovery. Front. Aging Neurosci. 13, 728799. 10.3389/fnagi.2021.728799

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 177

  Xu J. Li C. Li J. Zhang H. Jin H. Qu W. et al . (2017a). Classification of desired motion speed–a study based on cerebral hemoglobin information, in 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM) (Hefei and Tai'an: IEEE), 115–119.

  • Google Scholar

• 178

  Xu J. Li C. Zhang H. Li J. Jin H. Qu W. et al . (2017b). A pilot study based on cerebral hemoglobin information to classify the desired walking speed. IEEE Robot. Autom. Lett. 3, 532–536. 10.1109/LRA.2017.2765002

  • CrossRef
  • Google Scholar

• 179

  Yokoyama H. Kaneko N. Ogawa T. Kawashima N. Watanabe K. Nakazawa K. (2018). Cortical control of locomotor muscle activity through muscle synergies in humans: a neural decoding study. bioRxiv, 413567. 10.1101/413567

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 180

  Young S. R. Maddocks D. L. Caemmerer J. M. (2022). Computer-enhanced practice: The benefits of computer-assisted assessment in applied clinical practice. Profess. Psychol. Res. Pract. 2022, 449. 10.1037/pro0000449

  • CrossRef
  • Google Scholar

• 181

  Zeto (2023). ©Zeto. Available online at: https://zeto-inc.com/ (accessed February 14, 2023).

  • Google Scholar

• 182

  Zhu X. Korivand S. Hamill K. Jalili N. Gong J. (2022). A comprehensive decoding of cognitive load. Smart Health26, 100336. 10.1016/j.smhl.2022.100336

  • CrossRef
  • Google Scholar