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安裝中文字典英文字典辭典工具!
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- MNE — MNE 1. 9. 0 documentation - Aalto
Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more Distributed, sparse, mixed-norm, beamformers, dipole fitting, and more Advanced decoding models including time generalization Receptive field estimation with optional smoothness priors
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- Pybrain: M EEG analysis with MNE Python - YouTube
Richard Höchenberger's workshop on MNE Python, recorded 16-17 November, 2020 Workshop materials and notebooks: https: github com hoechenberger pybrain_mne 0
- mne·PyPI
MNE-Python is 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, connectivity analysis, machine learning, statistics, and more
- GitHub - mne-tools mne-python: MNE: Magnetoencephalography (MEG) and . . .
MNE-Python is 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, connectivity analysis, machine learning, statistics, and more
- MNE-Python — Neural Data Science in Python
MNE-Python is an open-source Python package for working with EEG and MEG data It was originally developed as a Python port (translation from one programming language to another) of a software package called MNE, that was written in the C language by MEG researcher Matti Hämäläinen
- The MNE-CPP Project
MNE-C - the original implementation of the MNE toolset in C What is MNE-CPP? MNE-CPP is both an open-source collection of cross-platform software tools and a framework for development We provide two main GUI applications, MNE Scan and MNE Analyze, as well as several command line tools
- Tutorials — MNE 1. 9. 0 documentation
These tutorials provide narrative explanations, sample code, and expected output for the most common MNE-Python analysis tasks The emphasis here is on thorough explanations that get you up to speed quickly, at the expense of covering only a limited number of topics
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