Package: bidsmreye
bidsMReyeBIDS app for decoding gaze position from the eyeball MR-signal usingdeepMReye(1).To be used on preprocessed BIDS derivatives (e.g.fMRIprepoutputs).
No eye-tracking data required.By default, bidsMReye uses apre-trained versionofdeepMReyetrained on 5 datasets incl.
guided fixations (2), smooth pursuit
(3,4,5)
and free viewing (6). Other
pretrained versions are optional. Dedicated model training is recommended.The pipeline automatically extracts the eyeball voxels.
This can be used also for other multivariate pattern
analyses in the absence of eye-tracking data.
Decoded gaze positions allow computing eye movements.Some basic quality control and outliers detection is also performed:for each runat the group levelFor more information, see theUser Recommendations.
If you have other questions, please reach out to the developer team.InstallBetter to use the docker image as there are known install issues
of deepmreye on Apple M1 for example.DockerBuilddockerbuild--tagcpplab/bidsmreye:latest--filedocker/Dockerfile.PullPull the latest docker image:dockerpullcpplab/bidsmreye:latestPython packageYou can also get the package from pypi if you want.pipinstallbidsmreyeConda installationNOT TESTED YETTo encapsulate bidsMReye in a virtual environment install with the following commands:condacreate--namebidsmreyepython=3.10
condaactivatebidsmreye
condainstallpip
pipinstallbidsmreyeThe tensorflow dependency supports both CPU and GPU instructions.Note that you might need to install cudnn firstcondainstall-cconda-forgecudnnANTsPy installation issuesIf installation ofANTsPyfails try to manually install it via:gitclonehttps://github.com/ANTsX/ANTsPycdANTsPy
pipinstallCMake
python3setup.pyinstallDev installClone this repository.gitclonegit://github.com/cpp-lln-lab/bidsmreyeThen install the package:cdbidsMReye
makeinstall_devUsageRequirementsbidsmreye requires your input fmri data:to be minimally preprocessed (at least realigned),with filenames and structure that conforms to a BIDS derivative dataset.Two bids apps are available to generate those types of preprocessed data:fmriprepbidspmObviousvly your fmri data must include the eyes of your participant for bidsmreye to work.CLIType the following for more information:bidsmreye--helpPreparing the data--action prepapremeans that bidsmreye will extract the data coming from the
eyes from the fMRI images.If your data is not in MNI space, bidsmreye will also register the data to MNI.bidsmreye--actionprepare\bids_dir\output_dir\participantComputing the eye movements--action generalizeuse the extracted timeseries to predict the eye movements
using the default pre-trained model of deepmreye.This will also generate a quality control report of the decoded eye movements.bidsmreye--actiongeneralize\bids_dir\output_dir\participantDoing it all at once--action alldoes "prepare" then "generalize".bidsmreye--actionall\bids_dir\output_dir\participantGroup level summarybidsmreye --action qcbids_diroutput_dirgroupDemoPlease look up thedocumentationContributors ✨Thanks goes to these wonderful people
(emoji key):Pauline Cabee💻🤔🚇Remi Gau💻🤔⚠️🚧This project follows theall-contributorsspecification. Contributions of any kind welcome!If you traindeepMReye, or if you have
eye-tracking training labels and the extracted eyeball voxels, consider sharing
it to contribute to thepretrained model pool.