Effects of brain-computer interface training on upper limb function recovery in stroke patients: A protocol for systematic review and meta-analysis

Effect of antibiotics on physical and physiological development of children under 5-A scoping review

The scoping review aimed to investigate and compile the effects of antibiotics on children under the age of five's physiological development. A PubMed, CINAHL, and Medline online database search was conducted, and related studies were included in the databases to carry out a more detailed search of the available literature utilizing keywords like "Antibiotics in children's"; "Children under 5"; and "Physiological Development, Physical Development," as well as Boolean operators to generate papers pertinent which were correlating with the objective of the study. It is imperative to demonstrate that a comprehensive, wide-ranging, and exhaustive search was carried out. MeSH words used for the search. MeSH is an is an effective tool for indexing and classifying literature on biology and health. MeSH terms are affixed to articles to enable precise and effective literature searches, guaranteeing that scholars, medical professionals, and other users can locate pertinent data within the extensive PubMed database. MeSH provides researchers with a standardized and structured method of indexing topics in the field of medicine and related disciplines, which aids in the identification and organization of pertinent articles during scoping reviews. PRISMA checklist was followed while doing the data collection and data extraction. The findings revealed that antibiotics hurt the physical and physiological development of children under 5. The study findings show that after exposure to antibiotics children get obese, it also affects the gut microbiota. Antibiotics also have an impact on the language and behaviors of children under 5. It also shows that children are more prone to get different medical disorders. These results highlight how crucial it is to make well-informed decisions about the use of antibiotics in pediatric care. To sum up, giving antibiotics to kids younger than five can have a big impact on how their bodies develop. This study also provides and implements guidelines that consider the possible long-term effects on the development of children under the age of five when prescribing antibiotics. Encourage healthcare professionals, parents, and other caregivers to learn about the proper use of antibiotics for young children as well as the possible risks of overusing or not using antibiotics at all. Promote funding and research for alternative approaches, such as targeted vaccines or probiotics, to treat and prevent infections in young children.

Cortical activity associated with focal muscle vibration applied directly to the affected forearm flexor muscle in post-stroke patients: an fNIRS study

Objective

The purpose of this study was to utilize functional near-infrared spectroscopy (fNIRS) to identify changes in cortical activity caused by focal muscle vibration (FMV), which was directly administered to the affected forearm flexor muscles of hemiplegic stroke patients. Additionally, the study aimed to investigate the correlation between these changes and the clinical characteristics of the patients, thereby expanding the understanding of potential neurophysiological mechanisms linked to these effects.

Methods

Twenty-two stroke patients with right hemiplegia who were admitted to our ward for rehabilitation were selected for this study. The fNIRS data were collected from subjects using a block-design paradigm. Subsequently, the collected data were analyzed using the NirSpark software to determine the mean Oxyhemoglobin (Hbo) concentrations for each cortical region of interest (ROI) in the task and rest states for every subject. The stimulation task was FMV (frequency 60 Hz, amplitude 6 mm) directly applied to belly of the flexor carpi radialis muscle (FCR) on the affected side. Hbo was measured in six regions of interest (ROIs) in the cerebral cortex, which included the bilateral prefrontal cortex (PFC), sensorimotor cortex (SMC), and occipital cortex (OC). The clinical characteristics of the patients were assessed concurrently, including Lovett's 6-level muscle strength assessment, clinical muscle tone assessment, the upper extremity function items of the Fugl-Meyer Assessment (FMA-UE), Bruunstrom staging scale (BRS), and Modified Barthel index (MBI). Statistical analyses were conducted to determine the activation in the ROIs and to comprehend its correlation with the clinical characteristics of the patients.

Results

Statistical analysis revealed that, except for right OC, there were statistically significant differences between the mean Hbo in the task state and rest state for bilateral SMC, PFC, and left OC. A positive correlation was observed between the muscle strength of the affected wrist flexor group and the change values of Hbo (Hbo-CV), as well as the beta values in the left SMC, PFC, and OC. However, no statistical correlation was found between muscle strength and Hbo-CV or beta values in the right SMC, PFC, and OC. The BRS of the affected upper limb exhibited a positive correlation with the Hbo-CV or beta values in the left SMC and PFC. In contrast, no statistical correlation was observed in the right SMC, PFC, and bilateral OC. No significant correlation was found between the muscle tone of the affected wrist flexor group, FMA-UE, MBI, and Hbo-CV or beta values of cortical ROIs.

Conclusion

FMV-evoked sensory stimulation applied directly to the FCR belly on the paralyzed side activated additional brain cortices, including bilateral PFC and ipsilesional OC, along with bilateral SMC in stroke patients. However, the clinical characteristics of the patients were only correlated with the intensity of ipsilesional SMC and PFC activation. The results of this study provide neurophysiological theoretical support for the expanded clinical application of FMV.

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI

Introduction

Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.

Methods

This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.

Results

A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.

Discussion

This approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery.

A Semi-Supervised Progressive Learning Algorithm for Brain-Computer Interface

Brain-computer interface (BCI) usually suffers from the problem of low recognition accuracy and large calibration time, especially when identifying motor imagery tasks for subjects with indistinct features and classifying fine grained motion control tasks by electroencephalogram (EEG)-electromyogram (EMG) fusion analysis. To fill the research gap, this paper presents an end-to-end semi-supervised learning framework for EEG classification and EEG-EMG fusion analysis. Benefiting from the proposed metric learning based label estimation strategy, sampling criterion and progressive learning scheme, the proposed framework efficiently extracts distinctive feature embedding from the unlabeled EEG samples and achieves a 5.40% improvement on BCI Competition IV Dataset IIa with 80% unlabeled samples and an average 3.35% improvement on two public BCI datasets. By employing synchronous EMG features as pseudo labels for the unlabeled EEG samples, the proposed framework further extracts deep level features of the synergistic complementarity between the EEG signals and EMG features based on the deep encoders, which improves the performance of hybrid BCI (with a 5.53% improvement for the Upper Limb Motion Dataset and an average 4.34% improvement on two hybrid datasets). Moreover, the ablation experiments show that the proposed framework can substantially improve the performance of the deep encoders (with an average 5.53% improvement). The proposed framework not only largely improves the performance of deep networks in the BCI system, but also significantly reduces the calibration time for EEG-EMG fusion analysis, which shows great potential for building an efficient and high-performance hybrid BCI for the motor rehabilitation process.

Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020

Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research.

Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words.

Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention.

Conclusions: At present, the brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.