Changelog#

MNE-NIRS follows semantic versioning. Such that, version numbers are described as v<MAJOR>.<MINOR>.<PATCH>. Major version changes indicate incompatible API changes. Minor version changes indicate new functionality was added in a backwards compatible manner. Patch version changes indicate backward compatible bug fixes.

To install a specific version of the library you would run pip install mne-nirs==0.3.0, where 0.3.0 is the version you wish to install.

v0.6.0#

  • Changed ci param to errorbar in sns-catplot functions. By Nicolas Busato.

  • Fix bug in SNIRF writer that caused incorrect duration to be written to file. By Robert Luke.

  • Add option to export montage location in SNIRF using the landmarkLabels field. By Robert Luke.

  • Fix continuous integration issues and update test infrastructure. By Florin Pop.

  • SNIRF writer uses v1.1 of the spec by default. By Florin Pop.

  • Migrate from PyQt5/PyQt6 to PySide6. By Florin Pop.

  • Add ability to write optical density data in SNIRF format. By Florin Pop.

  • Add ability to write haemoglobin data in SNIRF format. By Robert Luke.

v0.5.0#

v0.4.0#

  • Add support for reading auxiliary data from SNIRF files. By Robert Luke.

  • Fix bug in surface projection of HbR signals. By Johann Benerradi.

v0.3.0#

v0.2.1#

General

  • Maintenance release to align with MNE-Python 1.0 changes. By Alex Rockhill.

v0.2.0#

General

  • MNE-NIRS now requires the latest MNE-Python 1.0 or later.

Enhancements

Fixes

v0.1.2#

General

  • MNE-NIRS now uses the MNE-Python stable version v0.24 and no longer requires the development version.

Enhancements

v0.1.1#

API changes

Enhancements

Fixes

  • Fix end values for windowed quality metrics. By Robert Luke.

  • Fix snirf writer bug where it required the optional DateOfBirth field. By Christian Arthur, Jeonghoon Choi, Jiazhen Liu, and Juncheng Zhang

v0.1.0#

API changes

Adding a class simplifies user code and common use cases. To generate results in the new format, use the function run_glm rather than run_GLM. This will return a RegressionResults type that contains all relevant information. All previous functionality still exists with this new type, but is now accessible as more succinct methods that handle the relevant information, this results in less arguments being passed around by the user. For example, to access the previous glm_to_tidy(results) functionality use the new results.to_dataframe(). A full list of replacement methods is provided below.

Enhancements

Documentation

  • Add an example of how to include second level covariates in the group level GLM tutorial. By Robert Luke.

Bugs

Infrastructure

  • Change the git repository to use main, rather than master branch. By Robert Luke.

v0.0.6#

Enhancements

  • Added binder functionality to website tutorials. By Robert Luke.

  • Added convenience function for projecting GLM estimates to the cortical surface. By Robert Luke.

  • Improved Hitachi support. By Eric Larson.

v0.0.5#

Enhancements

  • Added windowed signal quality metrics scalp coupling index and peak power. By Robert Luke.

  • Added a finite impulse response (FIR) GLM example. By Robert Luke.

  • Added group level waveform example. By Robert Luke.

  • Added ability to use auto regressive models of any order in GLM computation. By Robert Luke.