SynthesisFrontiers in aging neuroscience2025
The EEG analysis and identification of Alzheimer's disease: a review.
Synthesis in Frontiers in aging neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Electroencephalography Functional Network Responses to Immersive Virtual Reality in Alzheimer Disease and Mild Cognitive Impairment: Exploratory Single-Session 3-Group Repeated-Measures Study.JMIR serious games · 2026Article
- High-density EEG network analysis in MCI: an exploratory study of electrode density and cognitive performance.Frontiers in aging neuroscience · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD), a neurodegenerative disorder, significantly impacts patients, families, and society. Therefore, efficient AD diagnosis and disease analysis are crucial. Electroencephalogram (EEG) directly reflects brain activity, making EEG-based AD identification a current research hotspot. This review utilized digital libraries (Google Scholar and PubMed) to categorize the literature into two sets based on different periods, ultimately analyzing the application of EEG in AD research through 141 articles after screening. Critical topics addressed include subject types, experimental design, electrode selection, artifact processing, rhythm division, feature extraction, recognition methods, etc. Additionally, the review discusses major conclusions, emphasizing research priorities and consistent findings. The study also briefly mentions other biomarkers and predicts future trends of EEG as a biomarker. This work provides valuable references for researchers and clinicians exploring the relationship between EEG and AD. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/.
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Registered trials
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.