Feature: Pupil Deconvolution (port from pyeparse) #12505
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This basically ports the code from pyeparse, with slight updates to work with mne and match mne and modern python conventions.
scott-huberty
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drammock,
agramfort and
dengemann
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scott-huberty
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A couple of nitpick, questions, but it looks great, don't forget to add a changelog entry!
Thanks @scott-huberty
larsoner
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Mar 19, 2024
From Mathieu and Eric Co-authored-by: Mathieu Scheltienne <mathieu.scheltienne@gmail.com> Co-authored-by: Eric Larson <larson.eric.d@gmail.com>
for more information, see https://pre-commit.ci
Was causing a circular import error in https://github.com/mne-tools/mne-python/actions/runs/8347127725/job/22846082215?pr=12505
Co-authored-by: Mathieu Scheltienne <mathieu.scheltienne@gmail.com>
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Reference issue
This PR implements the last feature proposed for my GSoC in #11879 that I didn't get to over the summer.
What does this implement/fix?
This basically ports the pupil deconvolution code from (the unmaintained?) https://github.com/pyeparse/pyeparse , and which was used for @larsoner and @drammock 's pupil deconvolution paper in 2016.
Additional information
I made some minor changes to the code and docstring to work with MNE's API and follow MNE conventions (and make the code more modern i.e. use format specifiers instead of percent specifiers).
To check that the ported code in this branch behaves similarly to the code in pyeparse, I re-ran the scripts to deconvolve the data and generate
figure 3in Dan and Eric's paper (The original scripts used the deconvolution method frompyeparse, and I updated the scripts to use the MNE API in this branch:Possible Alternatives
I have not opened an issue for deconvolution specifically, so if there are concerns about merging this into MNE or other ideas, don't hesitate to share.