%0 Journal Article
%T FDI: A MATLAB tool for computing the fractal dimension index of sources reconstructed from EEG data.
%A Ruiz de Miras J
%A Casali AG
%A Massimini M
%A Ibáñez-Molina AJ
%A Soriano MF
%A Iglesias-Parro S
%J Comput Biol Med
%V 179
%N 0
%D 2024 Sep 12
%M 39002315
%F 6.698
%R 10.1016/j.compbiomed.2024.108871
%X 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.