FDI: a MATLAB tool for computing the fractal dimension index of sources reconstructed from EEG data
Metadatos
Mostrar el registro completo del ítemAutor
Ruiz de Miras, Juan; Casali, Adenauer G.; Massimini, Marcello; Ibáñez Molina, Antonio J.; Soriano, María F.; Iglesias Parro, SergioEditorial
Elsevier
Materia
Fractal dimension EEG sources MATLAB CUDA
Fecha
2024-09-01Referencia bibliográfica
Juan Ruiz de Miras, Adenauer G. Casali, Marcello Massimini, Antonio J. Ibáñez-Molina, María F. Soriano, Sergio Iglesias-Parro, FDI: A MATLAB tool for computing the fractal dimension index of sources reconstructed from EEG data, Computers in Biology and Medicine, Volume 179, 2024, 108871, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2024.108871
Resumen
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: We introduce an open-source MATLAB software named FDI for measuring FDI values in EEG datasets.
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: With these resources, neuroscientists can readily apply FDI to investigate cortical activity complexity
within their own studies.