FDI: A MATLAB tool for computing the fractal dimension index of sources reconstructed from EEG data.

CUDA EEG sources Fractal dimension MATLAB

Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
12 Jul 2024
Historique:
received: 26 04 2024
revised: 08 07 2024
accepted: 08 07 2024
medline: 14 7 2024
pubmed: 14 7 2024
entrez: 13 7 2024
Statut: aheadofprint

Résumé

The fractal dimension (FD) is a valuable tool for analysing the complexity of neural structures and functions in the human brain. To assess the spatiotemporal complexity of brain activations derived from electroencephalogram (EEG) signals, the fractal dimension index (FDI) was developed. This measure integrates two distinct complexity metrics: 1) integration FD, which calculates the FD of the spatiotemporal coordinates of all significantly active EEG sources (4DFD); and 2) differentiation FD, determined by the complexity of the temporal evolution of the spatial distribution of cortical activations (3DFD), estimated via the Higuchi FD [HFD(3DFD)]. The final FDI value is the product of these two measurements: 4DFD × HFD(3DFD). Although FDI has shown utility in various research on neurological and neurodegenerative disorders, existing literature lacks standardized implementation methods and accessible coding resources, limiting wider adoption within the field. We introduce an open-source MATLAB software named FDI for measuring FDI values in EEG datasets. By using CUDA for leveraging the GPU massive parallelism to optimize performance, our software facilitates efficient processing of large-scale EEG data while ensuring compatibility with pre-processed data from widely used tools such as Brainstorm and EEGLab. Additionally, we illustrate the applicability of FDI by demonstrating its usage in two neuroimaging studies. Access to the MATLAB source code and a precompiled executable for Windows system is provided freely. With these resources, neuroscientists can readily apply FDI to investigate cortical activity complexity within their own studies.

Sections du résumé

BACKGROUND BACKGROUND
The fractal dimension (FD) is a valuable tool for analysing the complexity of neural structures and functions in the human brain. To assess the spatiotemporal complexity of brain activations derived from electroencephalogram (EEG) signals, the fractal dimension index (FDI) was developed. This measure integrates two distinct complexity metrics: 1) integration FD, which calculates the FD of the spatiotemporal coordinates of all significantly active EEG sources (4DFD); and 2) differentiation FD, determined by the complexity of the temporal evolution of the spatial distribution of cortical activations (3DFD), estimated via the Higuchi FD [HFD(3DFD)]. The final FDI value is the product of these two measurements: 4DFD × HFD(3DFD). Although FDI has shown utility in various research on neurological and neurodegenerative disorders, existing literature lacks standardized implementation methods and accessible coding resources, limiting wider adoption within the field.
METHODS METHODS
We introduce an open-source MATLAB software named FDI for measuring FDI values in EEG datasets.
RESULTS RESULTS
By using CUDA for leveraging the GPU massive parallelism to optimize performance, our software facilitates efficient processing of large-scale EEG data while ensuring compatibility with pre-processed data from widely used tools such as Brainstorm and EEGLab. Additionally, we illustrate the applicability of FDI by demonstrating its usage in two neuroimaging studies. Access to the MATLAB source code and a precompiled executable for Windows system is provided freely.
CONCLUSIONS CONCLUSIONS
With these resources, neuroscientists can readily apply FDI to investigate cortical activity complexity within their own studies.

Identifiants

pubmed: 39002315
pii: S0010-4825(24)00956-9
doi: 10.1016/j.compbiomed.2024.108871
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

108871

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Juan Ruiz de Miras (J)

Department of Software Engineering, University of Granada, Granada, Spain. Electronic address: demiras@ugr.es.

Adenauer G Casali (AG)

Institute of Science and Technology, Federal University of São Paulo, São Paulo, Brazil.

Marcello Massimini (M)

Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy; Fondazione Don Carlo Gnocchi, Milan, Italy.

Antonio J Ibáñez-Molina (AJ)

Department of Psychology, University of Jaén, Jaén, Spain.

María F Soriano (MF)

Mental Health Unit, San Agustín Hospital, Linares, Spain.

Sergio Iglesias-Parro (S)

Department of Psychology, University of Jaén, Jaén, Spain.

Classifications MeSH