topoEEG: An Python-framework for analyzing EEG data in neurodegeneratives disease through Topological Deep Learning

topoEEG is a Python framework designed for advanced EEG analysis, combining the MNE library with Topological Deep Learning (TDL) to enhance insights into neuroimaging, particularly for neurodegenerative diseases such as Alzheimer’s. The framework preprocesses EEG data by removing artifacts using Independent Component Analysis (ICA) and performs Power Spectral Density (PSD) analysis to identify critical frequency bands. By incorporating TDL, topoEEG uncovers topological features that traditional methods often overlook, offering deeper insights into neural activity. Unlike other standalone tools, it provides a unified solution, enhancing the accessibility of sophisticated analytics and supporting research in the diagnosis and understanding of neurodegenerative diseases.

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Publication Details

Journal
SoftwareX
Published
2025-06-21
DOI
https://doi.org/10.1016/j.softx.2025.102222
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

topoEEG: An Python-framework for analyzing EEG data in neurodegeneratives disease through Topological Deep Learning

Miriam Esteve, Antonio Falcó, J. J. Rodríguez-Sala, Alejandro Martinez-Gracia
SoftwareX
Functional Brain Connectivity Studies
article

topoEEG: An Python-framework for analyzing EEG data in neurodegeneratives disease through Topological Deep Learning

Miriam Esteve, Antonio Falcó, J. J. Rodríguez-Sala, Alejandro Martinez-Gracia
article en

Abstract

topoEEG is a Python framework designed for advanced EEG analysis, combining the MNE library with Topological Deep Learning (TDL) to enhance insights into neuroimaging, particularly for neurodegenerative diseases such as Alzheimer’s. The framework preprocesses EEG data by removing artifacts using Independent Component Analysis (ICA) and performs Power Spectral Density (PSD) analysis to identify critical frequency bands. By incorporating TDL, topoEEG uncovers topological features that traditional methods often overlook, offering deeper insights into neural activity. Unlike other standalone tools, it provides a unified solution, enhancing the accessibility of sophisticated analytics and supporting research in the diagnosis and understanding of neurodegenerative diseases.

SoftwareXVol. 31
Universitat de Miguel Hernández d'Elx (ES), Universidad Cardenal Herrera CEU (ES)
Ministerio de Ciencia, Innovación y Universidades, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación
Openalex Percentile: Top 86%
Functional Brain Connectivity Studies
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