Interpretable Machine Learning of Nonlinear EEG Dynamics Enables Robust Differentiation of Alzheimer's Disease and Frontotemporal Dementia
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| Autori principali: | , , , , |
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901712654041088 |
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| author | Salehi, Arman Heydarian, Ashkan Milani, Dornaz Mortazavy Beni, Hamidreza Kheirandish, parsa |
| author_facet | Salehi, Arman Heydarian, Ashkan Milani, Dornaz Mortazavy Beni, Hamidreza Kheirandish, parsa |
| contents | <p>This repository contains the complete computational pipeline for the study titled <strong>“Interpretable EEG-based classification of Alzheimer’s disease and frontotemporal dementia using nonlinear dynamics and explainable machine learning.”</strong> The project develops and validates a transparent machine learning framework to distinguish Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (FTD), and healthy controls (HC) using resting-state electroencephalography (EEG). The methodology emphasizes <strong>nonlinear dynamic features</strong> (e.g., recurrence quantification, Lyapunov exponents, multiscale entropy) and <strong>model interpretability</strong> via SHAP and topographic mapping.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18496634 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Interpretable Machine Learning of Nonlinear EEG Dynamics Enables Robust Differentiation of Alzheimer's Disease and Frontotemporal Dementia Salehi, Arman Heydarian, Ashkan Milani, Dornaz Mortazavy Beni, Hamidreza Kheirandish, parsa <p>This repository contains the complete computational pipeline for the study titled <strong>“Interpretable EEG-based classification of Alzheimer’s disease and frontotemporal dementia using nonlinear dynamics and explainable machine learning.”</strong> The project develops and validates a transparent machine learning framework to distinguish Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (FTD), and healthy controls (HC) using resting-state electroencephalography (EEG). The methodology emphasizes <strong>nonlinear dynamic features</strong> (e.g., recurrence quantification, Lyapunov exponents, multiscale entropy) and <strong>model interpretability</strong> via SHAP and topographic mapping.</p> |
| title | Interpretable Machine Learning of Nonlinear EEG Dynamics Enables Robust Differentiation of Alzheimer's Disease and Frontotemporal Dementia |
| url | https://doi.org/10.5281/zenodo.18496634 |