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

Embodiment for Robotic Lower-Limb Exoskeletons: A Narrative Review

Research on embodiment of objects external to the human body has revealed important information about how the human nervous system interacts with robotic lower limb exoskeletons. Typical robotic exoskeleton control approaches view the controllers as an external agent intending to move in coordination with the human. However, principles of embodiment suggest that the exoskeleton controller should ideally coordinate with the human such that the nervous system can adequately model the input-output dynamics of the exoskeleton controller. Measuring embodiment of exoskeletons should be a necessary step in the exoskeleton development and prototyping process. Researchers need to establish high fidelity quantitative measures of embodiment, rather than relying on current qualitative survey measures. Mobile brain imaging techniques, such as high-density electroencephalography, is likely to provide a deeper understanding of embodiment during human-machine interactions and advance exoskeleton research and development. In this review we show why future exoskeleton research should include quantitative measures of embodiment as a metric of success.

Sensorimotor Activities and Their Functional Connectivity Elicited by Robot-Assisted Passive Movements of Lower Limbs

Robot-assisted body movements are a useful approach for the rehabilitation of motor dysfunction. Various robots based on end-effector or exoskeleton type have been proposed. However, the effect of these robots on brain activity during assistive lower limb movements remains unclear. In this study, we evaluated brain activity results among robot-assisted passive movements, voluntary active movements, and kinesthetic motor imagery. We measured and compared the brain activities of 21 young, healthy individuals during three experimental conditions associated with lower limb movements (active, passive, and imagery conditions) using functional near-infrared spectroscopy (fNIRS). Our results showed that although different brain areas with significant activity were observed among the conditions, the temporal patterns of the activity in each recording channel and the spatial patterns of functional connectivity showed high similarity between robot-assisted passive movements and voluntary active movements. Conversely, the robot-assisted passive movements did not show any similarity to motor imagery. Overall, these findings suggest that the robotic assistive approach is useful for activating not only afferent processes associated with sensory feedback processing but also motor control-related efferent processes.

Mobile Electroencephalography for Studying Neural Control of Human Locomotion

Walking or running in real-world environments requires dynamic multisensory processing within the brain. Studying supraspinal neural pathways during human locomotion provides opportunities to better understand complex neural circuity that may become compromised due to aging, neurological disorder, or disease. Knowledge gained from studies examining human electrical brain dynamics during gait can also lay foundations for developing locomotor neurotechnologies for rehabilitation or human performance. Technical barriers have largely prohibited neuroimaging during gait, but the portability and precise temporal resolution of non-invasive electroencephalography (EEG) have expanded human neuromotor research into increasingly dynamic tasks. In this narrative mini-review, we provide a (1) brief introduction and overview of modern neuroimaging technologies and then identify considerations for (2) mobile EEG hardware, (3) and data processing, (4) including technical challenges and possible solutions. Finally, we summarize (5) knowledge gained from human locomotor control studies that have used mobile EEG, and (6) discuss future directions for real-world neuroimaging research.

Thoughts on neurophysiological signal analysis and classification

Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non‐invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non‐invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi‐modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.

Brain Functional Connectivity in Unconstrained Walking With and Without an Exoskeleton

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