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

Mining cross-frequency coupling microstates (CFCμstates) from EEG recordings during resting state and mental arithmetic tasks

The functional brain connectivity has been studied by analyzing synchronization between dynamic oscillations of identical frequency or between different frequencies of distinct brain areas. It has been hypothesized that cross-frequency coupling (CFC) between different frequency bands is the carrier mechanism for the coordination of global and local neural processes and hence supports the distributed information processing in the brain. In the present study, we attempt to study the dynamic evolution of CFC at resting-state and during a mental task. The concept of CFC microstates (CFCμstates) is introduced as emerged short-lived patterns of CFC. We analyzed dynamic CFC (dCFC) at resting-state and during a comparison task by adopting a phase-amplitude coupling (PAC) estimator for [δ phase-γ-amplitude] coupling at every sensor. Modifying a well-established framework for mining brain dynamics, we show that a small sized repertoire of CFCμstates can be derived so as to encapsulate connectivity variations and further provide novel insights into network's functional reorganization. By analyzing the transition dynamics among CFCμstates, in both tasks, we provided a clear evidence about intrinsic networks that may play a crucial role in information integration

Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant $(p<0.001)$ performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019 Conferenc

Guest Editorial

AFTER the tremendous clinical success of the cochlear implant over the last 20 years, neuroprosthetic systems are now being developed and applied for the blind. First results on implanted epiretinal arrays in humans are becoming available now and lead to clear suggestions of how to improve electrode design, device characteristics, and implant procedures. Besides implants in humans and animals, research on in vitro neuronal network systems is progressively expanding. Interesting combinations of multi-electrode array devices with microfluidic systems will allow pharmacological control of networks in a very precise way. Several papers in this Special Issue of the IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING are related to various neural neuroprosthetic systems. This Special Issue is a collective effort by active researchers who specialize in the field of neural engineering, and we hope it will provide a rich resource with regard to the state-of-the-art of neural engineering research