Current state and future prospects of EEG and fNIRS in robot-assisted gait rehabilitation : a brief review

Gait and balance impairments are frequently considered as the most significant concerns among individuals suffering from neurological diseases. Robot-assisted gait training (RAGT) has shown to be a promising neurorehabilitation intervention to improve gait recovery in patients following stroke or brain injury by potentially initiating neuroplastic changes. However, the neurophysiological processes underlying gait recovery through RAGT remain poorly understood. As non-invasive, portable neuroimaging techniques, electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide new insights regarding the neurophysiological processes occurring during RAGT by measuring different perspectives of brain activity. Due to spatial information about changes in cortical activation patterns and the rapid temporal resolution of bioelectrical changes, more features correlated with brain activation and connectivity can be identified when using fused EEG-fNIRS, thus leading to a detailed understanding of neurophysiological mechanisms underlying motor behavior and impairments due to neurological diseases. Therefore, multi-modal integrations of EEG-fNIRS appear promising for the characterization of neurovascular coupling in brain network dynamics induced by RAGT. In this brief review, we surveyed neuroimaging studies focusing specifically on robotic gait rehabilitation. While previous studies have examined either EEG or fNIRS with respect to RAGT, a multi-modal integration of both approaches is lacking. Based on comparable studies using fused EEG-fNIRS integrations either for guiding non-invasive brain stimulation (NIBS) or as part of brain-machine interface (BMI) paradigms, the potential of this methodologically combined approach in RAGT is discussed. Future research directions and perspectives for targeted, individualized gait recovery that optimize the outcome and efficiency of RAGT in neurorehabilitation were further derived

Effect of lower limb exoskeleton on the modulation of neural activity and gait classification

: Neurorehabilitation with robotic devices requires a paradigm shift to enhance human-robot interaction. The coupling of robot assisted gait training (RAGT) with a brain-machine interface (BMI) represents an important step in this direction but requires better elucidation of the effect of RAGT on the user's neural modulation. Here, we investigated how different exoskeleton walking modes modify brain and muscular activity during exoskeleton assisted gait. We recorded electroencephalographic (EEG) and electromyographic (EMG) activity from ten able-bodied volunteers walking with an exoskeleton with three modes of user assistance (i.e., transparent, adaptive and full assistance) and during free overground gait. Results identified that exoskeleton walking (irrespective of the exoskeleton mode) induces a stronger modulation of central mid-line mu (8-13 Hz) and low-beta (14-20 Hz) rhythms compared to free overground walking. These modifications are accompanied by a significant re-organization of the EMG patterns in exoskeleton walking. On the other hand, we observed no significant differences in neural activity during exoskeleton walking with the different assistance levels. We subsequently implemented four gait classifiers based on deep neural networks trained on the EEG data during the different walking conditions. Our hypothesis was that exoskeleton modes could impact the creation of a BMI-driven RAGT. We demonstrated that all classifiers achieved an average accuracy of 84.13 ± 3.49% in classifying swing and stance phases on their respective datasets. In addition, we demonstrated that the classifier trained on the transparent mode exoskeleton data can classify gait phases during adaptive and full modes with an accuracy of 78.3 ± 4.8%, while the classifier trained on free overground walking data fails to classify the gait during exoskeleton walking (accuracy of 59.4 ± 11.8%). These findings provide important insights into the effect of robotic training on neural activity and contribute to the advancement of BMI technology for improving robotic gait rehabilitation therapy

Arm swing in healthy and Parkinsonian gait:explorations on brain, muscle and movement level

Human bipedal gait exhibits a coherent four-limb movement pattern comparable to that observed in quadrupedal gait, with upper limbs swinging in anti-phase with both opposite upper and ipsilateral lower limbs. Although the role of these upper limb movements in bipedal gait is not as obvious as in quadrupedal gait, one proposed advantage concerns the modulation of neural control to maintain the cyclic gait pattern. This dissertation broadens the knowledge on this supporting role of arm swing in gait control in healthy participants and patients with Parkinson’s Disease (PD), a neurodegenerative disease that affects both lower-limb gait and gait-related arm swing. We used a multi-level approach including electroencephalography, electromyography and gait analyses to explore how this is organized within and between brain, muscle and movement level, respectively. We demonstrated that arm swing can drive and shape lower limb muscle activity via subcortical and cortical pathways, in which the supplementary motor area plays a central role. As a result of this neural interlimb coupling, we found that disturbed upper and lower limb movements in PD gait are correlated. These findings provide neural support for the observed facilitating effect of arm swing instructions on gait initiation and continued gait in PD patients. Overall, this dissertation supports that arm swing instructions or exercises could potentially be used as an effective non-invasive gait rehabilitation method in PD patients

Unilateral Exoskeleton Imposes Significantly Different Hemispherical Effect in Parietooccipital Region, but Not in Other Regions

In modern society, increasing people suffering from locomotor disabilities need an assistive exoskeleton to help them improve or restore ambulation. When walking is assisted by an exoskeleton, brain activities are altered as the closed-loop between brain and lower limbs is affected by the exoskeleton. Intuitively, a unilateral exoskeleton imposes differential effect on brain hemispheres (i.e., hemispherical effect) according to contralateral control mechanism. However, it is unclear whether hemispherical effect appears in whole hemisphere or particular region. To this end, we explored hemispherical effect on different brain regions using EEG data collected from 30 healthy participants during overground walking. The results showed that hemispherical effect was significantly different between regions when a unilateral exoskeleton was employed for walking assistance and no significance was observed for walking without the exoskeleton. Post-hoc t-test analysis revealed that hemispherical effect in the parietooccipital region significantly differed from other regions. In the parietooccipital region, a greater hemispherical effect was observed in beta band for exoskeleton-assisted walking compared to walking without exoskeleton, which was also found in the source analysis. These findings deepen the understanding of hemispherical effect of unilateral exoskeleton on brain and could aid the development of more efficient and suitable exoskeleton for walking assistance

Neural Underpinnings of Walking Under Cognitive and Sensory Load: A Mobile Brain/Body Imaging Approach

Dual-task walking studies, in which individuals engage in an attentionally-demanding task while walking, have provided indirect evidence via behavioral and biomechanical measures, of the recruitment of higher-level cortical resources during gait. Additionally, recent EEG and imaging (PET, fNIRS) studies have revealed direct neurophysiological evidence of cortical contributions to steady-state walking. However, there remains a lack of knowledge regarding the underlying neural mechanisms involved in the allocation of cortical resources while walking under increased load. This dissertation presents three experiments designed to provide a greater understanding of the cortical dynamics implicated in processing load (top-down or bottom-up) during locomotion. Furthermore, we seek to investigate age-related differences in these neural pathways. These studies were conducted using an innovative EEG-based Mobile Brain/Body Imaging (MoBI) approach, combining high-density EEG, foot force sensors and 3D body motion capture as participants walked on a treadmill. The first study employed a Go/No-Go response inhibition task to evaluate the long-term test-retest reliability of two cognitively-evoked event-related potentials (ERPs), the earlier N2 and the later P3. Acceptable levels of reliability were found, according to the intraclass correlation coefficient (ICC), and these were similar across sitting and walking conditions. Results indicate that electrocortical signals obtained during walking are stable indices of neurophysiological function. The aim of the second study was to characterize age-related changes in gait and in the allocation of cognitive control under single vs. dual-task load. For young adults, we observed significant modulations as a result of increased task load for both gait (longer stride time) and for ERPs (decreased N2 amplitude and P3 latency). In contrast, older adults exhibited costs in the cognitive domain (reduced accuracy performance), engaged in a more stereotyped pattern of walking, and showed a general lack of ERP modulation while walking under increased load, all of which may indicate reduced flexibility in resource allocation across tasks. Finally, the third study assessed the effects of sensory (optic flow and visual perturbations) and cognitive load (Go/No-Go task) manipulations on gait and cortical neuro-oscillatory activity in young adults. While walking under increased load, participants adopted a more conservative pattern of gait by taking shorter and wider strides, with cognitive load in particular associated with reduced motor variability. Using an Independent Component Analysis (ICA) and dipole-fitting approach, neuro-oscillatory activity was then calculated from eight source-localized clusters of Independent Components (ICs). Significant modulations in average spectral power in the theta (3-7Hz), alpha (8-12Hz), beta (13-30Hz), and gamma (31-45Hz) frequency bands were observed over occipital, parietal and frontal clusters of ICs, as a function of optic flow and task load. Overall, our findings demonstrate the reliability and feasibility of the MoBI approach to assess electrocortical activity in dual-task walking situations, and may be especially relevant to older adults who are less able to flexibly adjust to ongoing cognitive and sensory demands while walking

Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors

Neural coding of gait intent and continuous gait kinematics have advanced brain computer interface (BCI) technology for detection and predicting human upright walking movement. However, the dynamics of cortical involvement in upright walking and upright standing has not been clearly understood especially with the focus of off-laboratory assessments. In this study, wearable low-cost mobile phone accelerometers were used to extract position and velocity at 12 joints during walking and the cortical changes involved during gait phases of walking were explored using non-invasive electroencephalogram (EEG). Extracted gait data included, accelerometer values proximal to brachium of arm, antecubitis, carpus, coxal, femur and tarsus by considering physical parameters including height, weight and stride length. Including EEG data as features, the spectral and temporal features were used to classify and predict the swing and stance instances for healthy subjects. While focusing on stance and swing classification in healthy subjects, this chapter relates to gait features that help discriminate walking movement and its neurophysiological counterparts. With promising initial results, further exploration of gait may help change detection of movement neurological conditions in regions where specialists and clinical facilities may not be at par

Slow Potentials of the Sensorimotor Cortex during Rhythmic Movements of the Ankle

The objective of this dissertation was to more fully understand the role of the human brain in the production of lower extremity rhythmic movements. Throughout the last century, evidence from animal models has demonstrated that spinal reflexes and networks alone are sufficient to propagate ambulation. However, observations after neural trauma, such as a spinal cord injury, demonstrate that humans require supraspinal drive to facilitate locomotion. To investigate the unique nature of lower extremity rhythmic movements, electroencephalography was used to record neural signals from the sensorimotor cortex during three cyclic ankle movement experiments. First, we characterized the differences in slow movement-related cortical potentials during rhythmic and discrete movements. During the experiment, motion analysis and electromyography were used characterize lower leg kinematics and muscle activation patterns. Second, a custom robotic device was built to assist in passive and active ankle movements. These movement conditions were used to examine the sensory and motor cortical contributions to rhythmic ankle movement. Lastly, we explored the differences in sensory and motor contributions to bilateral, rhythmic ankle movements. Experimental results from all three studies suggest that the brain is continuously involved in rhythmic movements of the lower extremities. We observed temporal characteristics of the cortical slow potentials that were time-locked to the movement. The amplitude of these potentials, localized over the sensorimotor cortex, revealed a reduction in neural activity during rhythmic movements when compared to discrete movements. Moreover, unilateral ankle movements produced unique sensory potentials that tracked the position of the movement and motor potentials that were only present during active dorsiflexion. In addition, the spatiotemporal patterns of slow potentials during bilateral ankle movements suggest similar cortical mechanisms for both unilateral and bilateral movement. Lastly, beta frequency modulations were correlated to the movement-related slow potentials within medial sensorimotor cortex, which may indicate they are of similar cortical origin. From these results, we concluded that the brain is continuously involved in the production of lower extremity rhythmic movements, and that the sensory and motor cortices provide unique contributions to both unilateral and bilateral movemen

Cognitive Load Reduces the Effects of Optic Flow on Gait and 2 Electrocortical Dynamics During Treadmill Walking 3

While navigating complex environments the brain must continuously adapt to both external demands such as fluctuating sensory inputs, as well as internal demands, such as engagement in a cognitively demanding task. Previous studies have demonstrated changes in behavior and gait with increased sensory and cognitive load, but the underlying cortical mechanisms remain largely unknown. Here, in a Mobile Brain/Body Imaging (MoBI) approach sixteen young adults walked on a treadmill with high-density EEG while 3D motion capture tracked kinematics of the head and feet. Visual load was manipulated with the presentation of optic flow with and without continuous mediolateral perturbations. The effects of cognitive load were assessed by the performance of a Go/No-Go task on half of the blocks. During increased sensory load, participants walked with shorter and wider strides, which may indicate a more restrained pattern of gait. Interestingly, cognitive task engagement attenuated these effects of sensory load on gait. Using an Independent Component Analysis and dipole-fitting approach, we found that cautious gait was accompanied by neuro-oscillatory modulations localized to frontal (supplementary motor area, anterior cingulate cortex) and parietal (inferior parietal lobule, precuneus) areas. Our results show suppression in alpha/mu (8-12Hz) and beta (13-30Hz) rhythms, suggesting enhanced activation of these regions with unreliable sensory inputs. These findings provide insight into the neural correlates of gait adaptation, and may be particularly relevant to older adults who are less able to adjust to ongoing cognitive and sensory demands while walking