Abstract：
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.
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.
摘要：
背景：分形维数（FD）是分析人脑中神经结构和功能复杂性的有价值的工具。为了评估来自脑电图（EEG）信号的大脑激活的时空复杂性，建立了分形维数指数(FDI)。该度量集成了两个不同的复杂性度量：1)集成FD，计算所有显著活跃脑电图源的时空坐标的FD（4DFD）；和2）微分FD，由皮质激活空间分布的时间演变的复杂性（3DFD）决定，通过HiguchiFD[HFD（3DFD）]估算。最终的FDI值是这两个测量值的乘积:4DFD×HFD(3DFD)。尽管FDI在神经和神经退行性疾病的各种研究中显示出实用性，现有文献缺乏标准化的实现方法和可访问的编码资源，限制了该领域的广泛采用。
方法：我们介绍了一种名为FDI的开源MATLAB软件，用于测量EEG数据集中的FDI值。
结果：通过使用CUDA来利用GPU的大规模并行性来优化性能，我们的软件有助于有效处理大规模脑电图数据,同时确保与来自Brainstorm和EEGLab等广泛使用的工具的预处理数据的兼容性.此外,我们通过在两项神经影像学研究中展示其用途来说明FDI的适用性。免费提供对MATLAB源代码和Windows系统的预编译可执行文件的访问。
结论：有了这些资源，神经科学家可以很容易地应用FDI在他们自己的研究中调查皮质活动的复杂性。
方法：我们介绍了一种名为FDI的开源MATLAB软件，用于测量EEG数据集中的FDI值。
结果：通过使用CUDA来利用GPU的大规模并行性来优化性能，我们的软件有助于有效处理大规模脑电图数据,同时确保与来自Brainstorm和EEGLab等广泛使用的工具的预处理数据的兼容性.此外,我们通过在两项神经影像学研究中展示其用途来说明FDI的适用性。免费提供对MATLAB源代码和Windows系统的预编译可执行文件的访问。
结论：有了这些资源，神经科学家可以很容易地应用FDI在他们自己的研究中调查皮质活动的复杂性。
