2013. MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience 7. doi: 10.3389/ fnins.2013.00267, PMID: 24431986Gramfort A, Luessi M, Larson E, Engemann D, Strohmeier D, Brodbeck C, Goj R, Jas M, Brooks T, Parkkonen L, Hamalainen M (2013a) MEG and EEG data ...
数据分段 MNE-Python中的数据分段功能通过mne.Epochs类实现,通过Raw对象和事件信息的数组就能实例化一个Epochs对象。同时Epochs类中也附有卡阈值的功能,通过一个拒绝字典(rejection dictionary)实现,通过设置数据阈值的方式排除掉不好的试次的数据。 详细说明一下mne.Epochs实例化方法中的参数:raw代表传入的Raw对象,event...
MNE-Python的下载安装很简单,具体可参考https://mne.tools/stable/install/mne_python.html#installing-python。 加载数据 MNE支持多种不同类型的EEG和MEG数据的读取,相应的数据读取函数可以见下网址中列出: https://mne.tools/stable/overview/implementation.html#data-formats MNE同时也提供了大量公共数据,可以在下...
MNE-tools hosts a collection of software packages for analysis of (human) neuroimaging data, with emphasis on EEG, MEG, ECoG, iEEG, and fNIRS data. Limited support for MRI data is also provided, mostly for defining brain surfaces/volumes used to restrict inverse imaging of external (MEG) or...
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python - mne-tools/mne-python
MNE-Python MNE-Python softwareis an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivi...
MEG and EEG data analysis with MNE-python Front. Neurosci., 7 (2013), p. 267, 10.3389/fnins.2013.00267 View in ScopusGoogle Scholar Gramfort et al., 2014 A. Gramfort, M. Luessi, E. Larson, D.A. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, M.S. Hämäläinen MN...
A. Gramfort MEG and EEG data analysis with MNE-Python Front. Neurosci., 7 (2013), 10.3389/fnins.2013.00267 Google Scholar Gramfort et al., 2010 A. Gramfort, T. Papadopoulo, E. Olivi, M. Clerc OpenMEEG: opensource software for quasistatic bioelectromagnetics Biomed. Eng. Online, 9 (201...
mne-python:MNE:Python中的磁脑图(MEG)和脑电图(EEG)-源码 开发技术 - 其它 心愁**rⅡ上传62 MB文件格式zipvisualizationpythonmachine-learningstatisticsneuroscience mne-python:MNE:Python中的磁脑图(MEG)和脑电图(EEG) (0)踩踩(0) 所需:9积分
Previous work has shown that using face data for high-density meshes is sufficient for accurate co-registration of EEG and MEG13,14,15,16. After the initial alignment and vertex selection, the iterative closest point (ICP) algorithm with point-to-point minimisation is run for up to 100 ...