| Name | bidsmreye JSON |
| Version |
0.5.0
JSON |
| download |
| home_page | None |
| Summary | bids app using deepMReye to decode eye motion for fMRI time series data |
| upload_time | 2024-08-12 07:00:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9.0 |
| license | GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 bids app using deepMReye to decode eye motion for fMRI time series data Copyright (C) 2022 Remi Gau This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. Also add information on how to contact you by electronic and paper mail. You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>. The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>. |
| keywords |
bids
eyetracking
mri
machine learning
automated pipeline
brain imaging data structure
neuroimaging
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
absl-py
antspyx
anyio
argon2-cffi
argon2-cffi-bindings
arrow
astor
asttokens
astunparse
async-lru
attrs
babel
beautifulsoup4
bids-validator
bleach
cachetools
certifi
cffi
charset-normalizer
chart-studio
click
comm
contourpy
cycler
debugpy
decorator
deepmreye
defusedxml
docopt
executing
fastjsonschema
flatbuffers
fonttools
formulaic
fqdn
fsspec
gast
google-auth
google-auth-oauthlib
google-pasta
greenlet
grpcio
h11
h5py
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httpx
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isoduration
jedi
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pandas
pandocfilters
parso
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pexpect
pillow
platformdirs
plotly
pooch
prometheus-client
prompt-toolkit
protobuf
psutil
ptyprocess
pure-eval
pyasn1
pyasn1-modules
pybids
pycparser
pygments
pyparsing
python-dateutil
python-json-logger
pytz
pyyaml
pyzmq
qtconsole
qtpy
referencing
requests
requests-oauthlib
retrying
rfc3339-validator
rfc3986-validator
rich
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scikit-image
scikit-learn
scipy
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tenacity
tensorboard
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uri-template
urllib3
wcwidth
webcolors
webencodings
websocket-client
werkzeug
wheel
widgetsnbextension
wrapt
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/cpp-lln-lab/bidsMReye/actions/workflows/system_tests.yml)
[](https://github.com/cpp-lln-lab/bidsMReye/actions/workflows/test_and_coverage.yml)
[](https://codecov.io/gh/cpp-lln-lab/bidsMReye)
[](https://bidsmreye.readthedocs.io/en/latest/?badge=latest)
[](./LICENSE)
[](https://badge.fury.io/py/bidsmreye)
[](https://sourcery.ai)
[](#contributors)
[](https://doi.org/10.1038/s41593-021-00947-w)
[](https://doi.org/10.5281/zenodo.7493322)
# bidsMReye
BIDS app for decoding gaze position from the eyeball MR-signal using
[deepMReye](https://github.com/DeepMReye/DeepMReye)
([1](https://doi.org/10.1038/s41593-021-00947-w)).
To be used on preprocessed BIDS derivatives (e.g.
[fMRIprep](https://github.com/nipreps/fmriprep) outputs).
No eye-tracking data required.
By default, bidsMReye uses a [pre-trained version](https://osf.io/mrhk9/) of
[deepMReye](https://github.com/DeepMReye/DeepMReye) trained on 5 datasets incl.
guided fixations ([2](https://doi.org/10.1038/sdata.2017.181)), smooth pursuit
([3](https://doi.org/10.1016/j.neuroimage.2018.04.012),[4](https://doi.org/10.1101/2021.08.03.454928),[5](https://doi.org/10.1038/s41593-017-0050-8))
and free viewing ([6](https://doi.org/10.1038/s41593-017-0049-1)). 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 run
- at the group level
For more information, see the
[User Recommendations](https://deepmreye.slite.com/p/channel/MUgmvViEbaATSrqt3susLZ/notes/kKdOXmLqe).
If you have other questions, please reach out to the developer team.
## Install
Better to use the docker image as there are known install issues
of deepmreye on Apple M1 for example.
### Docker
#### Build
```bash
docker build --tag cpplab/bidsmreye:latest --file docker/Dockerfile .
```
#### Pull
Pull the latest docker image:
```bash
docker pull cpplab/bidsmreye:latest
```
### Python package
You can also get the package from pypi if you want.
```bash
pip install bidsmreye
```
#### Conda installation
**NOT TESTED YET**
To encapsulate bidsMReye in a virtual environment install with the following commands:
```bash
conda create --name bidsmreye python=3.10
conda activate bidsmreye
conda install pip
pip install bidsmreye
```
The tensorflow dependency supports both CPU and GPU instructions.
Note that you might need to install cudnn first
```bash
conda install -c conda-forge cudnn
```
### Dev install
Clone this repository.
```bash
git clone git://github.com/cpp-lln-lab/bidsmreye
```
Then install the package:
```bash
cd bidsMReye
make install_dev
```
## Usage
## Requirements
bidsmreye 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:
- [fmriprep](https://fmriprep.org/en/stable/)
- [bidspm](https://bidspm.readthedocs.io/en/latest/general_information.html)
Obviousvly your fmri data must include the eyes of your participant for bidsmreye to work.
<!-- old fmriprep versions may not work -->
### CLI
Type the following for more information:
```bash
bidsmreye --help
```
## Preparing the data
`prepapre` means 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.
```bash
bidsmreye bids_dir output_dir participant prepare
```
## Computing the eye movements
`generalize` use 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.
```bash
bidsmreye bids_dir output_dir participant generalize
```
## Doing it all at once
`all` does "prepare" then "generalize".
```bash
bidsmreye bids_dir output_dir participant all
```
## Group level summary
```
bidsmreye bids_dir output_dir group qc
```
## Demo
Please look up the [documentation](https://bidsmreye.readthedocs.io/en/latest/demo.html)
## Contributors ✨
Thanks goes to these wonderful people
([emoji key](https://allcontributors.org/docs/en/emoji-key)):
<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
<table>
<tbody>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://weexee.github.io/Portfolio/"><img src="https://avatars.githubusercontent.com/u/91776803?v=4?s=100" width="100px;" alt="Pauline Cabee"/><br /><sub><b>Pauline Cabee</b></sub></a><br /><a href="https://github.com/cpp-lln-lab/bidsMReye/commits?author=WeeXee" title="Code">💻</a> <a href="#ideas-WeeXee" title="Ideas, Planning, & Feedback">🤔</a> <a href="#infra-WeeXee" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://remi-gau.github.io/"><img src="https://avatars.githubusercontent.com/u/6961185?v=4?s=100" width="100px;" alt="Remi Gau"/><br /><sub><b>Remi Gau</b></sub></a><br /><a href="https://github.com/cpp-lln-lab/bidsMReye/commits?author=Remi-Gau" title="Code">💻</a> <a href="#ideas-Remi-Gau" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/cpp-lln-lab/bidsMReye/commits?author=Remi-Gau" title="Tests">⚠️</a> <a href="#maintenance-Remi-Gau" title="Maintenance">🚧</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/yyang1234"><img src="https://avatars.githubusercontent.com/u/59220868?v=4?s=100" width="100px;" alt="Ying Yang"/><br /><sub><b>Ying Yang</b></sub></a><br /><a href="https://github.com/cpp-lln-lab/bidsMReye/issues?q=author%3Ayyang1234" title="Bug reports">🐛</a> <a href="#userTesting-yyang1234" title="User Testing">📓</a></td>
</tr>
</tbody>
</table>
<!-- markdownlint-restore -->
<!-- prettier-ignore-end -->
<!-- ALL-CONTRIBUTORS-LIST:END -->
This project follows the
[all-contributors](https://github.com/all-contributors/all-contributors)
specification. Contributions of any kind welcome!
If you train [deepMReye](https://github.com/DeepMReye/DeepMReye), or if you have
eye-tracking training labels and the extracted eyeball voxels, consider sharing
it to contribute to the [pretrained model pool](https://osf.io/mrhk9/).
Raw data
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"description": "[](https://github.com/cpp-lln-lab/bidsMReye/actions/workflows/system_tests.yml)\n[](https://github.com/cpp-lln-lab/bidsMReye/actions/workflows/test_and_coverage.yml)\n[](https://codecov.io/gh/cpp-lln-lab/bidsMReye)\n[](https://bidsmreye.readthedocs.io/en/latest/?badge=latest)\n[](./LICENSE)\n[](https://badge.fury.io/py/bidsmreye)\n\n\n[](https://sourcery.ai)\n[](#contributors)\n[](https://doi.org/10.1038/s41593-021-00947-w)\n[](https://doi.org/10.5281/zenodo.7493322)\n\n\n\n# bidsMReye\n\nBIDS app for decoding gaze position from the eyeball MR-signal using\n[deepMReye](https://github.com/DeepMReye/DeepMReye)\n([1](https://doi.org/10.1038/s41593-021-00947-w)).\n\nTo be used on preprocessed BIDS derivatives (e.g.\n[fMRIprep](https://github.com/nipreps/fmriprep) outputs).\nNo eye-tracking data required.\n\nBy default, bidsMReye uses a [pre-trained version](https://osf.io/mrhk9/) of\n[deepMReye](https://github.com/DeepMReye/DeepMReye) trained on 5 datasets incl.\nguided fixations ([2](https://doi.org/10.1038/sdata.2017.181)), smooth pursuit\n([3](https://doi.org/10.1016/j.neuroimage.2018.04.012),[4](https://doi.org/10.1101/2021.08.03.454928),[5](https://doi.org/10.1038/s41593-017-0050-8))\nand free viewing ([6](https://doi.org/10.1038/s41593-017-0049-1)). Other\npretrained versions are optional. Dedicated model training is recommended.\n\nThe pipeline automatically extracts the eyeball voxels.\nThis can be used also for other multivariate pattern\nanalyses in the absence of eye-tracking data.\nDecoded gaze positions allow computing eye movements.\n\nSome basic quality control and outliers detection is also performed:\n\n- for each run\n\n\n\n\n- at the group level\n\n\n\nFor more information, see the\n[User Recommendations](https://deepmreye.slite.com/p/channel/MUgmvViEbaATSrqt3susLZ/notes/kKdOXmLqe).\nIf you have other questions, please reach out to the developer team.\n\n## Install\n\nBetter to use the docker image as there are known install issues\nof deepmreye on Apple M1 for example.\n\n### Docker\n\n#### Build\n\n```bash\ndocker build --tag cpplab/bidsmreye:latest --file docker/Dockerfile .\n```\n\n#### Pull\n\nPull the latest docker image:\n\n```bash\ndocker pull cpplab/bidsmreye:latest\n```\n\n### Python package\n\nYou can also get the package from pypi if you want.\n\n```bash\npip install bidsmreye\n```\n\n#### Conda installation\n\n**NOT TESTED YET**\n\nTo encapsulate bidsMReye in a virtual environment install with the following commands:\n\n```bash\nconda create --name bidsmreye python=3.10\nconda activate bidsmreye\nconda install pip\npip install bidsmreye\n```\n\nThe tensorflow dependency supports both CPU and GPU instructions.\n\nNote that you might need to install cudnn first\n\n```bash\nconda install -c conda-forge cudnn\n```\n\n### Dev install\n\nClone this repository.\n\n```bash\ngit clone git://github.com/cpp-lln-lab/bidsmreye\n```\n\nThen install the package:\n\n```bash\ncd bidsMReye\nmake install_dev\n```\n\n## Usage\n\n## Requirements\n\nbidsmreye requires your input fmri data:\n\n - to be minimally preprocessed (at least realigned),\n - with filenames and structure that conforms to a BIDS derivative dataset.\n\nTwo bids apps are available to generate those types of preprocessed data:\n\n- [fmriprep](https://fmriprep.org/en/stable/)\n- [bidspm](https://bidspm.readthedocs.io/en/latest/general_information.html)\n\nObviousvly your fmri data must include the eyes of your participant for bidsmreye to work.\n\n<!-- old fmriprep versions may not work -->\n\n### CLI\n\nType the following for more information:\n\n```bash\nbidsmreye --help\n```\n\n## Preparing the data\n\n`prepapre` means that bidsmreye will extract the data coming from the\neyes from the fMRI images.\n\nIf your data is not in MNI space, bidsmreye will also register the data to MNI.\n\n```bash\nbidsmreye bids_dir output_dir participant prepare\n```\n\n## Computing the eye movements\n\n`generalize` use the extracted timeseries to predict the eye movements\nusing the default pre-trained model of deepmreye.\n\nThis will also generate a quality control report of the decoded eye movements.\n\n```bash\nbidsmreye bids_dir output_dir participant generalize\n```\n## Doing it all at once\n\n`all` does \"prepare\" then \"generalize\".\n\n```bash\nbidsmreye bids_dir output_dir participant all\n```\n\n## Group level summary\n\n```\nbidsmreye bids_dir output_dir group qc\n```\n\n## Demo\n\nPlease look up the [documentation](https://bidsmreye.readthedocs.io/en/latest/demo.html)\n\n## Contributors \u2728\n\nThanks goes to these wonderful people\n([emoji key](https://allcontributors.org/docs/en/emoji-key)):\n\n<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->\n<!-- prettier-ignore-start -->\n<!-- markdownlint-disable -->\n<table>\n <tbody>\n <tr>\n <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://weexee.github.io/Portfolio/\"><img src=\"https://avatars.githubusercontent.com/u/91776803?v=4?s=100\" width=\"100px;\" alt=\"Pauline Cabee\"/><br /><sub><b>Pauline Cabee</b></sub></a><br /><a href=\"https://github.com/cpp-lln-lab/bidsMReye/commits?author=WeeXee\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#ideas-WeeXee\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#infra-WeeXee\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a></td>\n <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://remi-gau.github.io/\"><img src=\"https://avatars.githubusercontent.com/u/6961185?v=4?s=100\" width=\"100px;\" alt=\"Remi Gau\"/><br /><sub><b>Remi Gau</b></sub></a><br /><a href=\"https://github.com/cpp-lln-lab/bidsMReye/commits?author=Remi-Gau\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#ideas-Remi-Gau\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/cpp-lln-lab/bidsMReye/commits?author=Remi-Gau\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"#maintenance-Remi-Gau\" title=\"Maintenance\">\ud83d\udea7</a></td>\n <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/yyang1234\"><img src=\"https://avatars.githubusercontent.com/u/59220868?v=4?s=100\" width=\"100px;\" alt=\"Ying Yang\"/><br /><sub><b>Ying Yang</b></sub></a><br /><a href=\"https://github.com/cpp-lln-lab/bidsMReye/issues?q=author%3Ayyang1234\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"#userTesting-yyang1234\" title=\"User Testing\">\ud83d\udcd3</a></td>\n </tr>\n </tbody>\n</table>\n\n<!-- markdownlint-restore -->\n<!-- prettier-ignore-end -->\n\n<!-- ALL-CONTRIBUTORS-LIST:END -->\n\nThis project follows the\n[all-contributors](https://github.com/all-contributors/all-contributors)\nspecification. Contributions of any kind welcome!\n\nIf you train [deepMReye](https://github.com/DeepMReye/DeepMReye), or if you have\neye-tracking training labels and the extracted eyeball voxels, consider sharing\nit to contribute to the [pretrained model pool](https://osf.io/mrhk9/).\n",
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