Brain computer interface (BCI)-based training is promising for the treatment of stroke patients with upper limb (UL) paralysis. However, most stroke patients receive comprehensive treatment that not only includes BCI, but also routine training. The purpose of this study was to investigate the topological alterations in brain functional networks following comprehensive treatment, including BCI training, in the subacute stage of stroke. Twenty-five hospitalized subacute stroke patients with moderate to severe UL paralysis were assigned to one of two groups: 4-week comprehensive treatment, including routine and BCI training (BCI group, BG, n = 14) and 4-week routine training without BCI support (control group, CG, n = 11). Functional UL assessments were performed before and after training, including, Fugl-Meyer Assessment-UL (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT). Neuroimaging assessment of functional connectivity (FC) in the BG was performed by resting state functional magnetic resonance imaging. After training, as compared with baseline, all clinical assessments (FMA-UL, ARAT, and WMFT) improved significantly (p < 0.05) in both groups. Meanwhile, better functional improvements were observed in FMA-UL (p < 0.05), ARAT (p < 0.05), and WMFT (p < 0.05) in the BG. Meanwhile, FC of the BG increased across the whole brain, including the temporal, parietal, and occipital lobes and subcortical regions. More importantly, increased inter-hemispheric FC between the somatosensory association cortex and putamen was strongly positively associated with UL motor function after training. Our findings demonstrate that comprehensive rehabilitation, including BCI training, can enhance UL motor function better than routine training for subacute stroke patients. The reorganization of brain functional networks topology in subacute stroke patients allows for increased coordination between the Wu et al.Clinical and rs-fMRI Study multi-sensory and motor-related cortex and the extrapyramidal system. Future long-term, longitudinal, controlled neuroimaging studies are needed to assess the effectiveness of BCI training as an approach to promote brain plasticity during the subacute stage of stroke.

In the field of a relatively dangerous working environment, NASA developed Valkyrie, a 44-degree-of-freedom, series elastic actuator-based robot. In addition, Valkyrie is designed to respond to disasters like nuclear disasters and advance human spaceflight in extraterrestrial planetary settings. [6] By implementing safety features and allowing remote intervention, Atkeson et al. enabled an Atlas humanoid robot to meet the standard in performing disaster response-related tasks involving physical contact with the environment. [7] Currently, to allow humanoid robots to feel and process the environmental information as human beings for perfectly implementing tasks, perceptual comprehension and computation efficiency are two key indexes. Nevertheless, it is challenging to achieve them. The former requires the installation of massive, diverse sensors in humanoids, which will inevitably slow down the processing speed under the current sensing-storage-process separated framework, i.e., von Neumann architecture. In other words, the current computer architecture builds up a barrier between sensing performance and computation efficiency. In this context, neuromorphic devices which can emulate the perceptual and computation functions of the biological nervous system have illustrated their potential to break the von Neumann barrier, attracting researchers' interests (Figure 1). Humanoid robots, intelligent machines resembling the human body in shape and functions, cannot only replace humans to complete services and dangerous tasks but also deepen the own understanding of the human body in the mimicking process. Nowadays, attaching a large number of sensors to obtain more sensory information and efficient computation is the development trend for humanoid robots. Nevertheless, due to the constraints of von Neumann-based structures, humanoid robots are facing multiple challenges, including tremendous energy consumption, latency bottlenecks, and the lack of bionic properties. Memristors, featured with high similarity to the biological elements, play an important role in mimicking the biological nervous system. The memristor-based nervous system allows humanoid robots to obtain high energy efficiency and bionic sensing properties, which are similar properties to the biological nervous system. Herein, this article first reviews the biological nervous system and memristor-based nervous system thoroughly, including the structures and also the functions. The applications of memristor-based nervous systems are introduced, the difficulties that need to be overcome are put forward, and future development prospects are also discussed. This review can hopefully provide an evolutionary perspective on humanoid robots and memristor-based nervous systems.

The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N = 31) and healthy populations (N = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.