Metadata-Version: 2.1
Name: mne-nirs
Version: 0.0.5
Summary: An MNE compatible package for processing near-infrared spectroscopy data.
Home-page: https://mne.tools/mne-nirs/
Maintainer: Robert Luke
Maintainer-email: robert.luke@mq.edu.au
License: BSD (3-clause)
Download-URL: https://github.com/mne-tools/mne-nirs
Project-URL: Documentation, https://mne.tools/mne-nirs/
Project-URL: Source, https://github.com/mne-tools/mne-nirs/
Project-URL: Tracker, https://github.com/mne-tools/mne-nirs/issues/
Description: MNE-NIRS: Near-Infrared Spectroscopy Analysis
        =============================================
        
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        **MNE-NIRS** is an `MNE-Python <https://mne.tools>`_ compatible near-infrared spectroscopy processing package. 
        
        MNE-Python provides support for a subset of fNIRS waveform analysis, this package extends that functionality and adds additional GLM style analysis, helper functions, algorithms, data quality metrics, and plotting.
        
        
        Documentation
        -------------
        
        Documentation for this project is hosted `here <https://mne-tools.github.io/mne-nirs>`_.
        
        You can find a list of  `examples within the documentation <https://mne.tools/mne-nirs/master/auto_examples/index.html>`_.
        
        
        Features
        --------
        
        MNE-NIRS and MNE-Python provide a wide variety of tools to use when processing NIRS data including:
        
        * Loading data from a `wide variety of devices <https://mne.tools/mne-nirs/master/auto_examples/general/plot_01_data_io.html>`_, including `SNIRF files <https://mne.tools/mne-nirs/master/auto_examples/general/plot_19_snirf.html>`_.
        * `Apply 3D sensor locations <https://mne.tools/stable/overview/implementation.html#supported-formats-for-digitized-3d-locations>`_ from common digitisation systems such as Polhemus.
        * Standard preprocessing including `optical density calculation and Beer-Lambert Law conversion <https://mne.tools/mne-nirs/master/auto_examples/general/plot_15_waveform.html#id2>`_, filtering, etc.
        * Data quality metrics including `scalp coupling index <https://mne.tools/mne-nirs/master/auto_examples/general/plot_15_waveform.html#id3>`_ and `peak power <https://mne.tools/mne-nirs/master/auto_examples/general/plot_22_quality.html#peak-power>`_.
        * GLM analysis with a wide variety of cusomisation including `including FIR <https://mne.tools/mne-nirs/master/auto_examples/general/plot_13_fir_glm.html>`_ or `canonical HRF <https://mne.tools/mne-nirs/master/auto_examples/general/plot_11_hrf_measured.html>`_ analysis, higher order `autoregressive noise models <https://mne.tools/mne-nirs/master/auto_examples/general/plot_10_hrf_simulation.html#using-autoregressive-models-in-the-glm-to-account-for-noise-structure>`_, `short channel regression, region of interest analysis <https://mne.tools/mne-nirs/master/auto_examples/general/plot_11_hrf_measured.html>`_, etc.
        * Visualisation tools for all stages of processing from raw data to processed waveforms, GLM result visualisation, including both sensor and cortical surface projections.
        * Data cleaning functions including popular short channel techniques and negative correlation enhancement.
        * Group level analysis using `(robust) linear mixed effects models <https://mne.tools/mne-nirs/master/auto_examples/general/plot_12_group_glm.html>`_ and `waveform averaging <https://mne.tools/mne-nirs/master/auto_examples/general/plot_16_waveform_group.html>`_.
        * And much more! Check out the documentation `examples <https://mne.tools/mne-nirs/master/auto_examples/index.html>`_ and the API `for more details <https://mne.tools/mne-nirs/master/api.html>`_.
        
        
        Contributing
        ------------
        
        Contributions are welcome (more than welcome!). Contributions can be feature requests, improved documentation, bug reports, code improvements, new code, etc. Anything will be appreciated. *Note*: this package adheres to the same contribution  `standards as MNE <https://mne.tools/stable/install/contributing.html>`_.
        
        
        Acknowledgements
        ----------------
        
        This package is built on top of many other great packages. If you use MNE-NIRS you should also acknowledge these packages.
        
        MNE-Python: https://mne.tools/dev/overview/cite.html
        
        Nilearn: http://nilearn.github.io/authors.html#citing
        
        statsmodels: https://www.statsmodels.org/stable/index.html#citation
        
        Until there is a journal article specifically on MNE-NIRS, please cite `this article <https://www.biorxiv.org/content/10.1101/2020.12.22.423886v1>`_.
        
Keywords: neuroscience neuroimaging fNIRS NIRS brain
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/x-rst
Provides-Extra: tests
Provides-Extra: docs
