ArticleBiosensors & bioelectronics2025
Reactive EEG Biomarkers for Diagnosis and Prognosis of Alzheimer's Disease and Mild Cognitive Impairment.
Article in Biosensors & bioelectronics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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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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Who cites it
6 citing papers in PubMed.
- EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study.Alzheimer's research & therapy · 2026Article
- The Perioperative Neurocognitive Disorder Prediction Based on AI-Assisted EEG Dynamic Features in Anesthetized Mice.Diagnostics (Basel, Switzerland) · 2026Article
- Feature fusion and WOA-GWO optimization for Alzheimer's disease detection with sparse EEG channels.Frontiers in computational neuroscience · 2026Article
- A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.Cognitive neurodynamics · 2025Article
- Article
- Aperiodic EEG signatures: unveiling the interplay between APOE ε4 and mild cognitive impairment subtypes.Frontiers in aging neuroscience · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative condition characterized by progressive cognitive decline with currently no effective treatment available. One of the most critical areas in AD research is the identification of reliable biomarkers, which are essential for accurate diagnosis, prognostic assessment, and the development of targeted therapies. In this study, we introduce two novel reactive EEG (rEEG) biomarkers aimed at enhancing the diagnosis of AD and mild cognitive impairment (MCI). These biomarkers, previously unexplored in the literature, offer new insights into differentiating between various cognitive states. The first biomarker demonstrates a significant ability to distinguish between AD patients and normal controls (NC), while also effectively distinguishing MCI patients from NC. The second biomarker is designed to identify a subset of AD patients exhibiting hyperconductivity or hyperactivity, characterized by distinctive neural electrical patterns. A cohort of 90 elderly participants (mean age 76.63 ± 6.08 years) was recruited, including 30 AD patients, 30 individuals with MCI, and 30 NC subjects. Psychiatric diagnoses of participants were made by qualified professionals at Daejeon St. Mary's Hospital, The Catholic University of Korea, utilizing comprehensive neuropsychological assessments. Notably, the rEEG biomarkers achieved accuracies of 95%, 95%, and 98% in distinguishing between AD and NC, AD and MCI, and MCI and NC groups, respectively. These results underscore the potential of rEEG as a highly accurate and reliable diagnostic tool for cognitive impairments, including AD and MCI.
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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.