Research hotspots and trends of brain-computer interface technology in stroke: a bibliometric study and visualization analysis.

Li F ，Zhang D ，Chen J ，Tang K ，Li X ，Hou Z ... -  《Frontiers in Neuroscience》

Brain-Computer Interface for Patients with Spinal Cord Injury: A Bibliometric Study.

Spinal cord injury (SCI) is a debilitating condition with profound implications on patients' quality of life. Recent advancements in brain-computer interface (BCI) technology have provided novel opportunities for individuals with paralysis due to SCI. Consequently, research on the application of BCI for treating SCI has received increasing attention from scholars worldwide. However, there is a lack of rigorous bibliometric studies on the evolution and trends in this field. Hence, the present study aimed to use bibliometric methods to investigate the current status and emerging trends in the field of applying BCI for treating SCI and thus identify novel therapeutic options for SCI. We conducted a comprehensive review of the relevant literature on BCI applications for treating SCI published between 2005 and 2024 by using the Web of Science Core Collection database. To facilitate visualization and quantitative analysis of the published literature, we used VOSviewer and CiteSpace software tools. These tools enabled the assessment of co-authorships, co-occurrences, citations, and co-citations in the selected literature, thereby providing an overview of the current trends and predictive insights into the field. The literature search yielded 714 publications from the Web of Science Core Collection database. The findings indicated a significant upward trend in the number of publications, yielding a total of 24,804 citations, with an average citation rate of 34.74 per publication and an H-index of 75. Research contributions were identified from 54 countries/regions, and the United States, China, and Germany emerged as the predominant contributors. A total of 1114 research institutions contributed to the retrieved literature, with Harvard Medical School, Brown University, and Northwestern University producing the highest number of publications. The published literature was predominantly distributed across 258 academic journals, and the Journal of Neural Engineering was the most frequently utilized publication source. Hochberg, Leigh, Henderson, Jaimie, and Collinger were the prominent authors in this field. In recent years, there has been a steep increase in research on the use of BCI for treating SCI. Existing research focuses on the application of BCI for improving rehabilitation and quality of life of patients with SCI. Interdisciplinary collaboration is the current trend in this field.

Feng J ，Gao S ，Hu Y ，Sun G ，Sheng W ... -  《-》

Research Trends in the Application of Artificial Intelligence in Oncology: A Bibliometric and Network Visualization Study.

The past decade has seen major advances in the use of artificial intelligence (AI) to solve various biomedical problems, including cancer. This has resulted in more than 6000 scientific papers focusing on AI in oncology alone. The expansiveness of this research area presents a challenge to those seeking to understand how it has developed. A scientific analysis of AI in the oncology literature is therefore crucial for understanding its overall structure and development. This may be addressed through bibliometric analysis, which employs computational and visual tools to identify research activity, relationships, and expertise within large collections of bibliographic data. There is already a large volume of research data regarding the development of AI applications in cancer research. However, there is no published bibliometric analysis of this topic that offers comprehensive insights into publication growth, co-citation networks, research collaboration, and keyword co-occurrence analysis for technological trends involving AI across the entire spectrum of oncology research. The purpose of this study is to investigate documents published during the last decade using bibliometric indicators and network visualization. This will provide a detailed assessment of global research activities, key themes, and AI trends over the entire breadth of the oncology field. It will also specifically highlight top-performing authors, organizations, and nations that have made major contributions to this research domain, as well as their interactions via network collaboration maps and betweenness centrality metric. This study represents the first global investigation of AI covering the entire cancer field and using several validated bibliometric techniques. It should provide valuable reference material for reorienting this field and for identifying research trajectories, topics, major publications, and influential entities including scholars, institutions, and countries. It will also identify international collaborations at three levels: micro (that of an individual researcher), meso (that of an institution), and macro (that of a country), in order to inform future lines of research. The Science Citation Index Expanded from the Web of Science Core Collection was searched for articles and reviews pertaining exclusively to AI in cancer from 2012 through 2022. Annual publication trends were plotted using Microsoft Excel 2019. CiteSpace and VOSViewer were used to investigate the most productive countries, researchers, journals, as well as the sharing of resources, intellectual property, and knowledge base in this field, along with the co-citation analysis of references and keywords. A total of 6757 documents were retrieved. China produced the most publications of any country (2087, 30.89%), and Sun Yat Sen University the highest number (167, 2.47%) of any institute. WEI WANG was the most prolific author (33, 0.49%). RUI ZHANG ranked first for highest betweenness centrality (0.21) and collaboration criteria. Scientific Reports was found to be the most prolific journal (208, 3.18%), while PloS one had the most co-citations (2121, 1.55%). Strong and ongoing citation bursts were found for keywords such as "tissue microarray", "tissue segmentation", and "artificial neural network". Deep learning currently represents one of the most cutting-edge and applicable branches of AI in oncology. The literature to date has dealt extensively with radiomics, genomics, pathology, risk stratification, lesion detection, and therapy response. Current hot topics identified by our analysis highlight the potential application of AI in radiomics and precision oncology.

Wu T ，Duan Y ，Zhang T ，Tian W ，Liu H ，Deng Y ... -  《-》

Research trends and hotspots of post-stroke upper limb dysfunction: a bibliometric and visualization analysis.

The global prevalence of stroke has been increasing. Motor dysfunction is observed in approximately 55 to 75% of stroke patients, with upper limb impairment affecting around 85% of them. Following upper limb dysfunction, the body's recovery time is not only slower compared to the lower limbs, but the restoration of its fine motor skills is significantly more challenging, greatly impacting the daily lives of patients. Consequently, there is an increasing urgency for study on the upper limb function in stroke. A search was conducted in the Web of Science Core Collection: Science Citation Index Expanded (SCI-Expanded) database for material published from January 1, 2004 to December 31, 2023. We included all relevant literature reports and conducted an analysis of annual publications, countries/regions, institutions, journals, co-cited references, and keywords using the software packages CiteSpace, VOSviewer, and Bibliometrix R. Next, we succinctly outlined the research trends and hotspots in post-stroke upper limb dysfunction. This analysis comprised 1,938 articles from 1,897 institutions, 354 journals, and 53 countries or regions. A yearly rise in the production of publications was noted. The United States is the foremost nation on the issue. Northwestern University has the most amounts of papers compared to all other institutions. The journal Neurorehabilitation and Neural Repair is a highly significant publication in this field, with Catherine E. Lang serving as the principal author. The majority of the most-cited references focus on subjects such as the reliability and validity of assessment instruments, RCT of therapies, systematic reviews, and meta-analyses. The intervention measures primarily comprise three types of high-frequency phrases that are related, as determined by keyword analysis: intelligent rehabilitation, physical factor therapy, and occupational therapy. Current areas of focus in research include randomized clinical trials, neurorehabilitation, and robot-assisted therapy. Current research has shown a growing interest in studying upper limb function assessment, occupational therapy, physical therapy, robot-assisted therapy, virtual reality, brain-computer interface, telerehabilitation, cortical reorganisation, and neural plasticity. These topics have become popular and are expected to be the focus of future research.

Tang Q ，Yang X ，Sun M ，He M ，Sa R ，Zhang K ，Zhu B ，Li T ... -  《Frontiers in Neurology》

Bibliometric analysis of electroencephalogram research in Parkinson's disease from 2004 to 2023.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder affecting millions globally. It encompasses both motor and non-motor symptoms, with a notable impact on patients' quality of life. Electroencephalogram (EEG) is a non-invasive tool that is increasingly utilized to investigate neural mechanisms in PD, identify early diagnostic markers, and assess therapeutic responses. The data were sourced from the Science Citation Index Expanded within the Web of Science Core Collection database, focusing on publications related to EEG research in PD from 2004 to 2023. A comprehensive bibliometric analysis was conducted using CiteSpace and VOSviewer software. The analysis began with an evaluation of the selected publications, identifying leading countries, institutions, authors, and journals, as well as co-cited references, to summarize the current state of EEG research in PD. Keywords are employed to identify research topics that are currently of interest in this field through the analysis of high-frequency keyword co-occurrence and cluster analysis. Finally, burst keywords were identified to uncover emerging trends and research frontiers in the field, highlighting shifts in interest and identifying future research directions. A total of 1,559 publications on EEG research in PD were identified. The United States, Germany, and England have made notable contributions to the field. The University of London is the leading institution in terms of publication output, with the University of California closely following. The most prolific authors are Brown P, Fuhr P, and Stam C In terms of total citations and per-article citations, Stam C has the highest number of citations, while Brown P has the highest H-index. In terms of the total number of publications, Clinical Neurophysiology is the leading journal, while Brain is the most highly cited. The most frequently cited articles pertain to software toolboxes for EEG analysis, neural oscillations, and PD pathophysiology. Through analyzing the keywords, four research hotspots were identified: research on the neural oscillations and connectivity, research on the innovations in EEG Analysis, impact of therapies on EEG, and research on cognitive and emotional assessments. This bibliometric analysis demonstrates a growing global interest in EEG research in PD. The investigation of neural oscillations and connectivity remains a primary focus of research. The application of machine learning, deep learning, and task analysis techniques offers promising avenues for future research in EEG and PD, suggesting the potential for advancements in this field. This study offers valuable insights into the major research trends, influential contributors, and evolving themes in this field, providing a roadmap for future exploration.

Liao XY ，Gao YX ，Qian TT ，Zhou LH ，Li LQ ，Gong Y ，Ye TF ... -  《Frontiers in Neuroscience》