To enhance the treatment of motor function impairment, patients’ brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain–computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development.

Background We aimed to prepare a non‐invasive, reproducible, and controllable rat model of intracerebral hemorrhage with focused ultrasound (FUS). Methods A rat intracerebral hemorrhage (ICH) model was established by combining FUS and microbubbles (μBs), and edaravone was used to verify whether the free radical scavenger had a protective effect on the model. The brain tissue of each group was sectioned to observe the gross histology, blood–brain barrier (BBB) permeability, cerebral infarction volume, and histopathological changes. Results Compared with the FUS group, the BBB permeability was significantly increased in the FUS + μBs (F&B) group ( p = 0.0021). The second coronal slice in the F&B group had an obvious hemorrhage lesion, and the FUS + μBs + edaravone (F&B&E) group had smaller hemorrhage areas; however, ICH did not occur in the FUS group. The cerebral infarction volume in the F&B group was significantly larger than that in the FUS group ( p = 0.0030) and F&B&E group ( p = 0.0208). HE staining results showed that nerve fibrinolysis, neuronal necrosis, microglia production, and erythrocytes were found in both the F&B group and the F&B&E group, but the areas of the nerve fibrinolysis and neuronal necrosis in the F&B group were larger than the F&B&E group. Conclusions A rat ICH model was successfully prepared using the μBs assisted FUS treatment, and edaravone had a therapeutic effect on this model. This model can be used to study the pathophysiological mechanism of ICH‐related diseases and in preclinical research on related new drugs.

Logs is an important source of data in the field of security analysis. Log messages characterized by unstructured text, however, pose extreme challenges to security analysis. To this end, the first issue to be addressed is how to efficiently parse logs into structured data in real-time. The existing log parsers mostly parse raw log files by batch processing and are not applicable to real-time security analysis. It is also difficult to parse large historical log sets with such parsers. Some streaming log parsers also have some demerits in accuracy and parsing performance. To realize automatic, accurate, and efficient real-time log parsing, we propose Spray, a streaming log parser for real-time analysis. Spray can automatically identify the template of a real-time incoming log and accurately match the log and its template for parsing based on the law of contrapositive. We also improve Spray’s parsing performance based on key partitioning and search tree strategies. We conducted extensive experiments from such aspects as accuracy and performance. Experimental results show that Spray is much more accurate in parsing a variety of public log sets and has higher performance for parsing large log sets.