AbstractViewing behavior provides a window into many central aspects of human cognition and health, and it is an important variable of interest or confound in many fMRI studies. To make eye tracking freely and widely available for MRI research, we developed DeepMReye: a convolutional neural network that decodes gaze position from the MR-signal of the eyeballs. It performs camera-less eye tracking at sub-imaging temporal resolution in held-out participants with little training data and across a broad range of scanning protocols. Critically, it works even in existing datasets and when the eyes are closed. Decoded eye movements explain network-wide brain activity also in regions not associated with oculomotor function. This work emphasizes the importance of eye tracking for the interpretation of fMRI results and provides an open-source software solution that is widely applicable in research and clinical settings.
%0 Unpublished Work
%1 Frey2020-tq
%A Frey, Markus
%A Nau, Matthias
%A Doeller, Christian F
%D 2020
%J bioRxiv
%K
%T MR-based camera-less eye tracking using deep neural networks
%X AbstractViewing behavior provides a window into many central aspects of human cognition and health, and it is an important variable of interest or confound in many fMRI studies. To make eye tracking freely and widely available for MRI research, we developed DeepMReye: a convolutional neural network that decodes gaze position from the MR-signal of the eyeballs. It performs camera-less eye tracking at sub-imaging temporal resolution in held-out participants with little training data and across a broad range of scanning protocols. Critically, it works even in existing datasets and when the eyes are closed. Decoded eye movements explain network-wide brain activity also in regions not associated with oculomotor function. This work emphasizes the importance of eye tracking for the interpretation of fMRI results and provides an open-source software solution that is widely applicable in research and clinical settings.
@unpublished{Frey2020-tq,
abstract = {AbstractViewing behavior provides a window into many central aspects of human cognition and health, and it is an important variable of interest or confound in many fMRI studies. To make eye tracking freely and widely available for MRI research, we developed DeepMReye: a convolutional neural network that decodes gaze position from the MR-signal of the eyeballs. It performs camera-less eye tracking at sub-imaging temporal resolution in held-out participants with little training data and across a broad range of scanning protocols. Critically, it works even in existing datasets and when the eyes are closed. Decoded eye movements explain network-wide brain activity also in regions not associated with oculomotor function. This work emphasizes the importance of eye tracking for the interpretation of fMRI results and provides an open-source software solution that is widely applicable in research and clinical settings.},
added-at = {2024-09-10T11:56:37.000+0200},
author = {Frey, Markus and Nau, Matthias and Doeller, Christian F},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/274423af459dcb8262f6b5281ce04d222/scadsfct},
institution = {bioRxiv},
interhash = {d6c8f92bd26d347240cd78ccead3be53},
intrahash = {74423af459dcb8262f6b5281ce04d222},
journal = {bioRxiv},
keywords = {},
month = dec,
timestamp = {2024-09-10T15:15:57.000+0200},
title = {{MR-based} camera-less eye tracking using deep neural networks},
year = 2020
}
