MEG and EEG data analysis with MNE-Python.
MEG and EEG data analysis with MNE-Python.
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DOI：
10.3389/fnins.2013.00267
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发表时间：
2013-12-26
影响因子：
4.3
通讯作者：
Hämäläinen M
中科院分区：
文献类型：
--
作者：
Gramfort A;Luessi M;Larson E;Engemann DA;Strohmeier D;Brodbeck C;Goj R;Jas M;Brooks T;Parkkonen L;Hämäläinen M
脑磁图和脑电图（M/EEG）测量大脑中神经元活动产生的微弱电磁信号。使用这些信号来表征和定位大脑中的神经激活是一项挑战，需要物理学，信号处理，统计学和数值方法的专业知识。作为MNE软件套件的一部分，MNE-Python是一个开源软件包，通过提供Python中实现的最先进的算法来解决这一挑战，这些算法涵盖了数据预处理，源定位，统计分析和分布式大脑区域之间功能连接的估计等多种方法。所有算法和实用程序功能都以一致的方式实现，具有良好的文档界面，使用户能够通过编写Python脚本创建M/EEG数据分析管道。此外，MNE-Python与核心Python库紧密集成，用于科学计算（NumPy，SciPy）和可视化（matplotlib和Mayavi），以及通过Nibabel包在Python中更大的神经成像生态系统。代码是在新的BSD许可证下提供的，允许代码重用，即使在商业产品中也是如此。虽然MNE-Python只经历了几年的大规模开发，但它已经随着扩展的分析功能和教学教程而迅速发展，因为多个实验室在代码开发过程中进行了合作，以帮助共享最佳实践。MNE-Python还提供了对预处理数据集的轻松访问，帮助用户快速入门，并促进其他研究人员的方法重现性。完整的文档，包括几十个例子，可以在http://martinos.org/mne上找到。
Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.
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影响因子：
--
作者：
Dalal SS;Zumer JM;Guggisberg AG;Trumpis M;Wong DD;Sekihara K;Nagarajan SS
通讯作者：
Nagarajan SS
影响因子：
3.5
作者：
Gorgolewski K;Burns CD;Madison C;Clark D;Halchenko YO;Waskom ML;Ghosh SS
通讯作者：
Ghosh SS
影响因子：
5.7
作者：
Fischl, B;Sereno, MI;Dale, AM
通讯作者：
Dale, AM
影响因子：
4.6
作者：
Gramfort, Alexandre;Keriven, Renaud;Clerc, Maureen
通讯作者：
Clerc, Maureen
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y