Department of Software Engineering, University of Granada, Granada, Spain.
Institute of Science and Technology, Federal University of São Paulo, São Paulo, Brazil.
Comput Biol Med. 2024 Sep;179:108871. doi: 10.1016/j.compbiomed.2024.108871. Epub 2024 Jul 15.
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.
分形维数（FD）是分析人类大脑中神经结构和功能复杂性的有用工具。为了评估来自脑电图（EEG）信号的大脑激活的时空复杂性，开发了分形维数指数（FDI）。该度量标准综合了两个不同的复杂度指标：1）积分 FD，计算所有显著活动 EEG 源的时空坐标的 FD（4DFD）；2）分化 FD，由皮质激活的时空分布的时间演化复杂性决定（3DFD），通过 Higuchi FD [HFD(3DFD)] 估计。最终的 FDI 值是这两个测量值的乘积：4DFD×HFD(3DFD)。尽管 FDI 在各种神经和神经退行性疾病的研究中表现出了实用性，但现有文献缺乏标准化的实施方法和可访问的编码资源，限制了该方法在该领域的广泛应用。
我们引入了一个名为 FDI 的开源 MATLAB 软件，用于测量 EEG 数据集的 FDI 值。
通过使用 CUDA 利用 GPU 的大规模并行性来优化性能，我们的软件实现了对大规模 EEG 数据的高效处理，同时确保与广泛使用的工具（如 Brainstorm 和 EEGLab）预处理后的数据兼容。此外，我们通过展示其在两项神经影像学研究中的应用，说明了 FDI 的适用性。提供了免费的 MATLAB 源代码和适用于 Windows 系统的预编译可执行文件。
有了这些资源，神经科学家可以方便地在自己的研究中应用 FDI 来研究皮质活动的复杂性。
