A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects
on006036 · 37 high-confidence citations
- A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG
- CNN-based framework for Alzheimer's disease detection from EEG via dynamic mode decomposition
- From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Visual Concepts in Brain Signal Analysis
- NeuroFusionNet: a hybrid EEG feature fusion framework for accurate and explainable Alzheimer’s Disease detection
- EEG spectral power differences in Alzheimer’s disease and frontotemporal dementia
- Dynamic Mode Decomposition-Based Clustered Pattern Projection for Reliable Alzheimer’s Disease Detection from EEG
- A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer’s classification
- Deep Learning-Based Alzheimer’s Disease Detection from Multi-Channel EEG Using Fused Time–Frequency Image Grids
- LEAD: An EEG Foundation Model for Alzheimer's Disease Detection
- Dynamic Signatures of Dementia: Task-Augmented EEG Classification of Alzheimer's Disease
- Alzheimer's/FTD EEG Classifier Test Suite (code v5)
- Clustered pattern projection for EEG dementia classification: evaluating the reliability of disorder patterns
- Multidomain Photostimulation EEG Biomarkers: Neural Entrainment Signatures for Differential Dementia Phenotyping
- Graph-theoretical analysis of resting-state EEG networks differentiates Alzheimer’s disease and frontotemporal dementia
- EEG-Based Detection of Alzheimer’s Disease and Frontotemporal Dementia Using Transformer Networks
- Dynamic Signatures of Dementia: A Task-Evoked Five-Dimensional EEG Classifier for Alzheimer's Disease
- Quantifying Diagnostic Uncertainty in EEG-Based Dementia Classification Using Conformal Prediction
- EEG-MSAF: An Interpretable Microstate Framework uncovers Default-Mode Decoherence in Early Neurodegeneration
- Explainable EEG-based machine learning for early diagnosis of Alzheimer’s disease and frontotemporal dementia
- Selective 10-Hz photic driving deficit shared between Alzheimer's and frontotemporal dementia
- Índice de Complexidade Collatz (ICC): Um Novo Biomarcador EEG para Doenças Neurodegenerativas
- Índice de Complexidade Collatz (ICC): Um Novo Biomarcador EEG para Doenças Neurodegenerativas
- Alzheimer's/FTD EEG Classifier Test Suite (code v5)
- Analysis code and results for: Dynamic Signatures of Dementia: A Task-Evoked Five-Dimensional EEG Classifier for Alzheimer's Disease
- Code and trained models for: HC-Anchored Rank Normalization for Cross-Cohort EEG Classification of Alzheimer's Disease
- HC-Anchored Rank Normalization for Cross-Cohort EEG Classification of Alzheimer's Disease: An Open Methodology with Held-Out Validation
- HC-Anchored Rank Normalization for Cross-Cohort EEG Classification of Alzheimer's Disease: An Open Methodology with Held-Out Validation
- HC-Anchored Rank Normalization for Cross-Cohort EEG Classification of Alzheimer's Disease: An Open Methodology with Held-Out Validation
- Code and trained models for: HC-Anchored Rank Normalization for Cross-Cohort EEG Classification of Alzheimer's Disease
- Analysis code and results for: Dynamic Signatures of Dementia: A Task-Evoked Five-Dimensional EEG Classifier for Alzheimer's Disease
- Dynamic Signatures of Dementia: A Task-Evoked Five-Dimensional EEG Classifier for Alzheimer's Disease
- Dynamic Signatures of Dementia: Task-Augmented EEG Classification of Alzheimer's Disease
- Code and trained models for: HC-Anchored Rank Normalization for Cross-Cohort EEG Classification of Alzheimer's Disease
- HC-Anchored Rank Normalization for Cross-Cohort EEG Classification of Alzheimer's Disease: An Open Methodology with Held-Out Validation
- Deep Neural EEG Based Alzeihmers Detection
- Computational Decomposition of New Memory Failure in Alzheimers Disease Through a Hippocampal Cortical Consolidation Bottleneck Model
- Time Series Machine Learning for Classifying Electroencephalograms
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