ReviewBioMed research international2021
A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals.
Review in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Task-specific dynamical entropy variations in EEG as a biomarker for Parkinson's disease progression.GeroScience · 2026Article
- NeuroFusionNet: a hybrid EEG feature fusion framework for accurate and explainable Alzheimer's Disease detection.Scientific reports · 2025Article
- Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Dynamic reconstruction of electroencephalogram data using RBF neural networks.Frontiers in neuroscience · 2025Article
- The top 100 cited articles on artificial intelligence in Alzheimer's disease and mild cognitive impairment: a bibliometric analysis.Frontiers in aging neuroscience · 2025Article
- EEG entropy insights in the context of physiological aging and Alzheimer's and Parkinson's diseases: a comprehensive review.GeroScience · 2024Review
- Investigating Cortical Complexity in Mixed Dementia through Nonlinear Dynamic Analyses: A Resting-State EEG Study.Iranian journal of psychiatry · 2024Article
- Neural markers of reduced arousal and consciousness in mild cognitive impairment.International journal of geriatric psychiatry · 2024Article
- Article
- EEG Complexity Analysis of Brain States, Tasks and ASD Risk.Advances in neurobiology · 2024Article
- Article
- Identifying biomarkers for tDCS treatment response in Alzheimer's disease patients: a machine learning approach using resting-state EEG classification.Frontiers in human neuroscience · 2023Article
- GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.
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Registered trials
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