ArticleFrontiers in aging neuroscience2026
The application of traditional machine learning and deep learning with EEG for mild cognitive impairment: a bibliometric analysis.
Article in Frontiers in aging neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
Objective: This study uses bibliometric methods to analyze the status of electroencephalography (EEG) and machine learning applications in mild cognitive impairment (MCI) research. Methods: Relevant literature was searched in the four major English databases (Web of Science, PubMed, IEEE Xplore, and Scopus) and the three major Chinese databases (CNKI, Wanfang, and VIP) from their inception to June 2026. After removing duplicates using NoteExpress and screening the literature according to the inclusion and exclusion criteria, the final data were imported into CiteSpace 6.4. R2 and VOSviewer 1.6.20 for analysis of publication trends, geographic distribution, highly co-cited references, keyword clustering, and burst detection. Results: A total of 547 valid studies were included in the analysis, comprising 523 English-language studies and 24 Chinese core journal articles. The number of publications in this field has shown phased growth. It entered a period of rapid development after 2018. China ranks first globally in terms of English-language publications; however, there is a significant gap between the number of core publications in Chinese and English. Conclusion: Electroencephalography combined with machine learning has become an important research direction for the early identification and assessment of MCI. The research focus has shifted from traditional EEG signal analysis and manual feature engineering to optimization of deep learning models and exploration of multimodal data fusion. Improved algorithm interpretability, refined EEG rhythm and brain region localization, and the combination of neuropsychological assessment and EEG indicators have become a new research trend.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.