Negligible Motion Artifacts in Scalp Electroencephalography (EEG) During Treadmill Walking

Transient visual perturbations boost short-term balance learning in virtual reality by modulating electrocortical activity

Immersive virtual reality can expose humans to novel training and sensory environments, but motor training with virtual reality has not been able to improve motor performance as much as motor training in real-world conditions. An advantage of immersive virtual reality that has not been fully leveraged is that it can introduce transient visual perturbations on top of the visual environment being displayed. The goal of this study was to determine whether transient visual perturbations introduced in immersive virtual reality modify electrocortical activity and behavioral outcomes in human subjects practicing a novel balancing task during walking. We studied three groups of healthy young adults (5 male and 5 female for each) while they learned a balance beam walking task for 30 min under different conditions. Two groups trained while wearing a virtual reality headset, and one of those groups also had half-second visual rotation perturbations lasting ~10% of the training time. The third group trained without virtual reality. We recorded high-density electroencephalography (EEG) and movement kinematics. We hypothesized that virtual reality training with perturbations would increase electrocortical activity and improve balance performance compared with virtual reality training without perturbations. Our results confirmed the hypothesis. Brief visual perturbations induced increased theta spectral power and decreased alpha spectral power in parietal and occipital regions and improved balance performance in posttesting. Our findings indicate that transient visual perturbations during immersive virtual reality training can boost short-term motor learning by inducing a cognitive change, minimizing the negative effects of virtual reality on motor training. NEW & NOTEWORTHY We found that transient visual perturbations in virtual reality during balance training can boost short-term motor learning by inducing a cognitive change, overcoming the negative effects of immersive virtual reality. As a result, subjects training in immersive virtual reality with visual perturbations have equivalent performance improvement as training in real-world conditions. Visual perturbations elicited cortical responses in occipital and parietal regions and may have improved the brain's ability to adapt to variations in sensory input.

Changes in Mental Workload and Motor Performance Throughout Multiple Practice Sessions Under Various Levels of Task Difficulty

The allocation of mental workload is critical to maintain cognitive-motor performance under various demands. While mental workload has been investigated during performance, limited efforts have examined it during cognitive-motor learning, while none have concurrently manipulated task difficulty. It is reasonable to surmise that the difficulty level at which a skill is practiced would impact the rate of skill acquisition and also the rate at which mental workload is reduced during learning (relatively slowed for challenging compared to easier tasks). This study aimed to monitor mental workload by assessing cortical dynamics during a task practiced under two difficulty levels over four days while perceived task demand, performance, and electroencephalography (EEG) were collected. As expected, self-reported mental workload was reduced, greater working memory engagement via EEG theta synchrony was observed, and reduced cortical activation, as indexed by progressive EEG alpha synchrony was detected during practice. Task difficulty was positively related to the magnitude of alpha desynchrony and accompanied by elevations in the theta-alpha ratio. Counter to expectation, the absence of an interaction between task difficulty and practice days for both theta and alpha power indicates that the refinement of mental processes throughout learning occurred at a comparable rate for both levels of difficulty. Thus, the assessment of brain dynamics was sensitive to the rate of change of cognitive workload with practice, but not to the degree of difficulty. Future work should consider a broader range of task demands and additional measures of brain processes to further assess this phenomenon.

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

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

Dynamics of corticospinal motor control during overground and treadmill walking in humans

Increasing evidence suggests cortical involvement in the control of human gait. However, the nature of corticospinal interactions remains poorly understood. We performed time-frequency analysis of electrophysiological activity acquired during treadmill and overground walking in 22 healthy, young adults. Participants walked at their preferred speed (4.2, SD 0.4 km/h), which was matched across both gait conditions. Event-related power, corticomuscular coherence (CMC), and intertrial coherence (ITC) were assessed for EEG from bilateral sensorimotor cortices and EMG from the bilateral tibialis anterior (TA) muscles. Cortical power, CMC, and ITC at theta, alpha, beta, and gamma frequencies (4-45 Hz) increased during the double support phase of the gait cycle for both overground and treadmill walking. High beta (21-30 Hz) CMC and ITC of EMG was significantly increased during overground compared with treadmill walking, as well as EEG power in theta band (4-7 Hz). The phase spectra revealed positive time lags at alpha, beta, and gamma frequencies, indicating that the EEG response preceded the EMG response. The parallel increases in power, CMC, and ITC during double support suggest evoked responses at spinal and cortical populations rather than a modulation of ongoing corticospinal oscillatory interactions. The evoked responses are not consistent with the idea of synchronization of ongoing corticospinal oscillations but instead suggest coordinated cortical and spinal inputs during the double support phase. Frequency-band dependent differences in power, CMC, and ITC between overground and treadmill walking suggest differing neural control for the two gait modalities, emphasizing the task-dependent nature of neural processes during human walking. NEW & NOTEWORTHY We investigated cortical and spinal activity during overground and treadmill walking in healthy adults. Parallel increases in power, corticomuscular coherence, and intertrial coherence during double support suggest evoked responses at spinal and cortical populations rather than a modulation of ongoing corticospinal oscillatory interactions. These findings identify neurophysiological mechanisms that are important for understanding cortical control of human gait in health and disease.

Cortical activity during walking and balance tasks in older adults and in people with Parkinson's disease: A structured review

An emerging body of literature has examined cortical activity during walking and balance tasks in older adults and in people with Parkinson's disease, specifically using functional near infrared spectroscopy (fNIRS) or electroencephalography (EEG). This review provides an overview of this developing area, and examines the disease-specific mechanisms underlying walking or balance deficits. Medline, PubMed, PsychInfo and Scopus databases were searched. Articles that described cortical activity during walking and balance tasks in older adults and in those with PD were screened by the reviewers. Thirty-seven full-text articles were included for review, following an initial yield of 566 studies. This review summarizes study findings, where increased cortical activity appears to be required for older adults and further for participants with PD to perform walking and balance tasks, but specific activation patterns vary with the demands of the particular task. Studies attributed cortical activation to compensatory mechanisms for underlying age- or PD-related deficits in automatic movement control. However, a lack of standardization within the reviewed studies was evident from the wide range of study protocols, instruments, regions of interest, outcomes and interpretation of outcomes that were reported. Unstandardized data collection, processing and reporting limited the clinical relevance and interpretation of study findings. Future work to standardize approaches to the measurement of cortical activity during walking and balance tasks in older adults and people with PD with fNIRS and EEG systems is needed, which will allow direct comparison of results and ensure robust data collection/reporting. Based on the reviewed articles we provide clinical and future research recommendations.

A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface

We present a mobile brain-body imaging (MoBI) dataset acquired during treadmill walking in a brain-computer interface (BCI) task. The data were collected from eight healthy subjects, each having three identical trials. Each trial consisted of three conditions: standing, treadmill walking, and treadmill walking with a closed-loop BCI. During the BCI condition, subjects used their brain activity to control a virtual avatar on a screen to walk in real-time. Robust procedures were designed to record lower limb joint angles (bilateral hip, knee, and ankle) using goniometers synchronized with 60-channel scalp electroencephalography (EEG). Additionally, electrooculogram (EOG), EEG electrodes impedance, and digitized EEG channel locations were acquired to aid artifact removal and EEG dipole-source localization. This dataset is unique in that it is the first published MoBI dataset recorded during walking. It is useful in addressing several important open research questions, such as how EEG is coupled with gait cycle during closed-loop BCI, how BCI influences neural activity during walking, and how a BCI decoder may be optimized.

Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion

More neuroscience researchers are using scalp electroencephalography (EEG) to measure electrocortical dynamics during human locomotion and other types of movement. Motion artifacts corrupt the EEG and mask underlying neural signals of interest. The cause of motion artifacts in EEG is often attributed to electrode motion relative to the skin, but few studies have examined EEG signals under head motion. In the current study, we tested how motion artifacts are affected by the overall mass and surface area of commercially available electrodes, as well as how cable sway contributes to motion artifacts. To provide a ground-truth signal, we used a gelatin head phantom with embedded antennas broadcasting electrical signals, and recorded EEG with a commercially available electrode system. A robotic platform moved the phantom head through sinusoidal displacements at different frequencies (0–2 Hz). Results showed that a larger electrode surface area can have a small but significant effect on improving EEG signal quality during motion and that cable sway is a major contributor to motion artifacts. These results have implications in the development of future hardware for mobile brain imaging with EEG.

Real-time EEG-based brain-computer interface to a virtual avatar enhances cortical involvement in human treadmill walking

Recent advances in non-invasive brain-computer interface (BCI) technologies have shown the feasibility of neural decoding for both users' gait intent and continuous kinematics. However, the dynamics of cortical involvement in human upright walking with a closed-loop BCI has not been investigated. This study aims to investigate the changes of cortical involvement in human treadmill walking with and without BCI control of a walking avatar. Source localization revealed significant differences in cortical network activity between walking with and without closed-loop BCI control. Our results showed sustained α/µ suppression in the Posterior Parietal Cortex and Inferior Parietal Lobe, indicating increases of cortical involvement during walking with BCI control. We also observed significant increased activity of the Anterior Cingulate Cortex (ACC) in the low frequency band suggesting the presence of a cortical network involved in error monitoring and motor learning. Additionally, the presence of low γ modulations in the ACC and Superior Temporal Gyrus may associate with increases of voluntary control of human gait. This work is a further step toward the development of a novel training paradigm for improving the efficacy of rehabilitation in a top-down approach.

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

In lower mammals, locomotion seems to be mainly regulated by subcortical and spinal networks. On the contrary, recent evidence suggests that in humans the motor cortex is also significantly engaged during complex locomotion tasks. However, a detailed understanding of cortical contribution to locomotion is still lacking especially during stereotyped activities. Here, we show that cortical motor areas finely control leg muscle activation during treadmill stereotyped walking. Using a novel technique based on a combination of Reliable Independent Component Analysis, source localization and effective connectivity, and by combining electroencephalographic (EEG) and electromyographic (EMG) recordings in able-bodied adults we were able to examine for the first time cortical activation patterns and cortico-muscular connectivity including information flow direction. Results not only provided evidence of cortical activity associated with locomotion, but demonstrated significant causal unidirectional drive from contralateral motor cortex to muscles in the swing leg. These insights overturn the traditional view that human cortex has a limited role in the control of stereotyped locomotion, and suggest useful hypotheses concerning mechanisms underlying gait under other conditions.

ONE SENTENCE SUMMARY

Motor cortex proactively drives contralateral swing leg muscles during treadmill walking, counter to the traditional view of stereotyped human locomotion.

Walking-Related Dual-Task Interference in Early-to-Middle-Stage Huntington's Disease: An Auditory Event Related Potential Study

Objective: To compare interference between walking and a simple P3 auditory odd-ball paradigm in patients with Huntington's disease (HD) and age- and sex-matched controls.

Methods: Twenty-four early-to-middle-stage HD patients and 14 age- and sex-matched healthy volunteers were examined. EEG—EMG recordings were obtained from 21 scalp electrodes and eight bipolar derivations from the legs. Principal component analysis was used to obtain artifact-free recordings. The stimulation paradigm consisted of 50 rare and 150 frequent stimuli and was performed in two conditions: standing and walking along a 10 by 5 m path. P3 wave amplitude and latency and EEG and EMG spectral values were compared by group and experimental condition and correlated with clinical features of HD.

Results: P3 amplitude increased during walking in both HD patients and controls. This effect was inversely correlated with motor impairment in HD patients, who showed a beta-band power increase over the parieto-occipital regions in the walking condition during the P3 task. Walking speed and counting of rare stimuli were not compromised by concurrence of motor and cognitive demands.

Conclusion: Our results showed that walking increased P3 amplitude in an auditory task, in both HD patients and controls. Concurrent cognitive and motor stimulation could be used for rehabilitative purposes as a means of enhancing activation of cortical compensatory reserves, counteracting potential negative interference and promoting the integration of neuronal circuits serving different functions.

Independent Component Analysis and Source Localization on Mobile EEG Data Can Identify Increased Levels of Acute Stress

Mobile electroencephalography (EEG) is a very useful tool to investigate the physiological basis of cognition under real-world conditions. However, as we move experimentation into less-constrained environments, the influence of state changes increases. The influence of stress on cortical activity and cognition is an important example. Monitoring of modulation of cortical activity by EEG measurements is a promising tool for assessing acute stress. In this study, we test this hypothesis and combine EEG with independent component analysis and source localization to identify cortical differences between a control condition and a stressful condition. Subjects performed a stationary shooting task using an airsoft rifle with and without the threat of an experimenter firing a different airsoft rifle in their direction. We observed significantly higher skin conductance responses and salivary cortisol levels (p < 0.05 for both) during the stressful conditions, indicating that we had successfully induced an adequate level of acute stress. We located independent components in five regions throughout the cortex, most notably in the dorsolateral prefrontal cortex, a region previously shown to be affected by increased levels of stress. This area showed a significant decrease in spectral power in the theta and alpha bands less than a second after the subjects pulled the trigger. Overall, our results suggest that EEG with independent component analysis and source localization has the potential of monitoring acute stress in real-world environments.

Estimation of Human Workload from the Auditory Steady-State Response Recorded via a Wearable Electroencephalography System during Walking

Workload in the human brain can be a useful marker of internal brain state. However, due to technical limitations, previous workload studies have been unable to record brain activity via conventional electroencephalography (EEG) and magnetoencephalography (MEG) devices in mobile participants. In this study, we used a wearable EEG system to estimate workload while participants walked in a naturalistic environment. Specifically, we used the auditory steady-state response (ASSR) which is an oscillatory brain activity evoked by repetitive auditory stimuli, as an estimation index of workload. Participants performed three types of N-back tasks, which were expected to command different workloads, while walking at a constant speed. We used a binaural 500 Hz pure tone with amplitude modulation at 40 Hz to evoke the ASSR. We found that the phase-locking index (PLI) of ASSR activity was significantly correlated with the degree of task difficulty, even for EEG data from few electrodes. Thus, ASSR appears to be an effective indicator of workload during walking in an ecologically valid environment.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

TRIAL REGISTRATION

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

Long-term test-retest reliability of event-related potential (ERP) recordings during treadmill walking using the mobile brain/body imaging (MoBI) approach

Advancements in acquisition technology and signal-processing techniques have spurred numerous recent investigations on the electro-cortical signals generated during whole-body motion. This approach, termed Mobile Brain/Body Imaging (MoBI), has the potential to elucidate the neural correlates of perceptual and cognitive processes during real-life activities, such as locomotion. However, as of yet, no one has assessed the long-term stability of event-related potentials (ERPs) recorded under these conditions. Therefore, the objective of the current study was to evaluate the test-retest reliability of cognitive ERPs recorded while walking. High-density EEG was acquired from 12 young adults on two occasions, separated by an average of 2.3years, as they performed a Go/No-Go response inhibition paradigm. During each testing session, participants performed the task while walking on a treadmill and seated. Using the intraclass correlation coefficient (ICC) as a measure of agreement, we focused on two well-established neurophysiological correlates of cognitive control, the N2 and P3 ERPs. Following ICA-based artifact rejection, the earlier N2 yielded good to excellent levels of reliability for both amplitude and latency, while measurements for the later P3 component were generally less robust but still indicative of adequate to good levels of stability. Interestingly, the N2 was more consistent between walking sessions, compared to sitting, for both hits and correct rejection trials. In contrast, the P3 waveform tended to have a higher degree of consistency during sitting conditions. Overall, these results suggest that the electro-cortical signals obtained during active walking are representative of stable indices of neurophysiological function.

A Channel Rejection Method for Attenuating Motion-Related Artifacts in EEG Recordings during Walking

Recording scalp electroencephalography (EEG) during human motion can introduce motion artifacts. Repetitive head movements can generate artifact patterns across scalp EEG sensors. There are many methods for identifying and rejecting bad channels and independent components from EEG datasets, but there is a lack of methods dedicated to evaluate specific intra-channel amplitude patterns for identifying motion-related artifacts. In this study, we proposed a template correlation rejection (TCR) as a novel method for identifying and rejecting EEG channels and independent components carrying motion-related artifacts. We recorded EEG data from 10 subjects during treadmill walking. The template correlation rejection method consists of creating templates of amplitude patterns and determining the fraction of total epochs presenting relevant correlation to the template. For EEG channels, the template correlation rejection removed channels presenting the majority of epochs (>75%) correlated to the template, and presenting pronounced amplitude in comparison to all recorded channels. For independent components, the template correlation rejection removed components presenting the majority of epochs correlated to the template. Evaluation of scalp maps and power spectra confirmed low neural content for the rejected components. We found that channels identified for rejection contained ~60% higher delta power, and had spectral properties locked to the gait phases. After rejecting the identified channels and running independent component analysis on the EEG datasets, the proposed method identified 4.3 ± 1.8 independent components (out of 198 ± 12) with substantive motion-related artifacts. These results indicate that template correlation rejection is an effective method for rejecting EEG channels contaminated with motion-related artifact during human locomotion.

Neuroimaging of Human Balance Control: A Systematic Review

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

Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals

With the development of Brain Machine Interface (BMI) systems, people with motor disabilities are able to control external devices to help them restore movement abilities. Longitudinal validation of these systems is critical not only to assess long-term performance reliability but also to investigate adaptations in electrocortical patterns due to learning to use the BMI system. In this paper, we decode the patterns of user's intended gait states (e.g., stop, walk, turn left, and turn right) from scalp electroencephalography (EEG) signals and simultaneously learn the relative importance of different brain areas by using the multiple kernel learning (MKL) algorithm. The region of importance (ROI) is identified during training the MKL for classification. The efficacy of the proposed method is validated by classifying different movement intentions from two subjects—an able-bodied and a spinal cord injury (SCI) subject. The preliminary results demonstrate that frontal and fronto-central regions are the most important regions for the tested subjects performing gait movements, which is consistent with the brain regions hypothesized to be involved in the control of lower-limb movements. However, we observed some regional changes comparing the able-bodied and the SCI subject. Moreover, in the longitudinal experiments, our findings exhibit the cortical plasticity triggered by the BMI use, as the classification accuracy and the weights for important regions—in sensor space—generally increased, as the user learned to control the exoskeleton for movement over multiple sessions.

Control of an Ambulatory Exoskeleton with a Brain-Machine Interface for Spinal Cord Injury Gait Rehabilitation

The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation).

Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain-computer interface to a virtual reality avatar

OBJECTIVE

The control of human bipedal locomotion is of great interest to the field of lower-body brain-computer interfaces (BCIs) for gait rehabilitation. While the feasibility of closed-loop BCI systems for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a BCI virtual reality (BCI-VR) environment has yet to be demonstrated. BCI-VR systems provide valuable alternatives for movement rehabilitation when wearable robots are not desirable due to medical conditions, cost, accessibility, usability, or patient preferences.

APPROACH

In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control a walking avatar in a virtual environment. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1-3 Hz) were used for prediction; thus, the EEG features correspond to time-domain amplitude modulated potentials in the delta band. Virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days.

MAIN RESULTS

Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. The average decoding accuracies (Pearson's r values) in real-time BCI across all subjects increased from (Hip: 0.18 ± 0.31; Knee: 0.23 ± 0.33; Ankle: 0.14 ± 0.22) on Day 1 to (Hip: 0.40 ± 0.24; Knee: 0.55 ± 0.20; Ankle: 0.29 ± 0.22) on Day 8.

SIGNIFICANCE

These findings have implications for the development of a real-time closed-loop EEG-based BCI-VR system for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI-VR system.

Cortical Spectral Activity and Connectivity during Active and Viewed Arm and Leg Movement

Active and viewed limb movement activate many similar neural pathways, however, to date most comparison studies have focused on subjects making small, discrete movements of the hands and feet. The purpose of this study was to determine if high-density electroencephalography (EEG) could detect differences in cortical activity and connectivity during active and viewed rhythmic arm and leg movements in humans. Our primary hypothesis was that we would detect similar but weaker electrocortical spectral fluctuations and effective connectivity fluctuations during viewed limb exercise compared to active limb exercise due to the similarities in neural recruitment. A secondary hypothesis was that we would record stronger cortical spectral fluctuations for arm exercise compared to leg exercise, because rhythmic arm exercise would be more dependent on supraspinal control than rhythmic leg exercise. We recorded EEG data while ten young healthy subjects exercised on a recumbent stepper with: (1) both arms and legs, (2) just legs, and (3) just arms. Subjects also viewed video playback of themselves or another individual performing the same exercises. We performed independent component analysis, dipole fitting, spectral analysis, and effective connectivity analysis on the data. Cortical areas comprising the premotor and supplementary motor cortex, the anterior cingulate, the posterior cingulate, and the parietal cortex exhibited significant spectral fluctuations during rhythmic limb exercise. These fluctuations tended to be greater for the arms exercise conditions than for the legs only exercise condition, which suggests that human rhythmic arm movements are under stronger cortical control than rhythmic leg movements. We did not find consistent spectral fluctuations in these areas during the viewed conditions, but effective connectivity fluctuated at harmonics of the exercise frequency during both active and viewed rhythmic limb exercise. The right premotor and supplementary motor cortex drove the network. These results suggest that a similarly interconnected neural network is in operation during active and viewed human rhythmic limb movement.