ArticleFrontiers in neurology2023
Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis.Frontiers in aging neuroscience · 2025Pooled it
- Integrating attractor dynamics and connectivity features for EEG-based dementia classification.Scientific reports · 2026Article
- Interpretable AI for neural signal decoding in dementia: an EEG ensemble approach to differential diagnosis.Frontiers in neuroinformatics · 2026Article
- Obsessive-compulsive disorder detection using ensemble of scalp EEG-based convolutional neural network.Physical and engineering sciences in medicine · 2025Article
- Modeling Working Memory in Neurodegeneration: A Focus on EEG Methods.Diagnostics (Basel, Switzerland) · 2025Review
- Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer's Disease Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.Scientific reports · 2025Article
- Replication and validation of two novel magnetoencephalography functional connectivity measures in Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
5 authors.
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Abstract
Introduction: Dementia is a neurological disorder associated with aging that can cause a loss of cognitive functions, impacting daily life. Alzheimer's disease (AD) is the most common cause of dementia, accounting for 50-70% of cases, while frontotemporal dementia (FTD) affects social skills and personality. Electroencephalography (EEG) provides an effective tool to study the effects of AD on the brain. Methods: In this study, we propose to use shallow neural networks applied to two sets of features: spectral-temporal and functional connectivity using four methods. We compare three supervised machine learning techniques to the CNN models to classify EEG signals of AD / FTD and control cases. We also evaluate different measures of functional connectivity from common EEG frequency bands considering multiple thresholds. Results and discussion: Results showed that the shallow CNN-based models achieved the highest accuracy of 94.54% with AEC in test dataset when considering all connections, outperforming conventional methods and providing potentially an additional early dementia diagnosis tool.
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