Kling, S. M. | bioRxiv Science

bioRxiv2026

Optimizing MR-based gaze-decoding for eyes-closed eye-tracking in fMRI

Eye movements provide valuable insights into human cognition and are a critical variable in numerous functional magnetic resonance imaging (fMRI) studies. Yet, when the eyes are closed, camera-based eye-tracking is unavailable, making studies of eyes-closed states challenging. Here, we address this gap using DeepMReye, a deep learning framework for camera-free gaze reconstruction from the MR-signal of the eyes. We first show that fine-tuning DeepMReye on visuomotor calibration data acquired with the eyes open significantly improves gaze decoding, and that this fine-tuning does not require simultaneous camera-based data. We next assessed whether decoding could be extended to eyes-closed states using a novel auditory-guided task, in which participants gazed at learned target positions with and without visual input, and with their eyes open, blinking, or closed. While DeepMReye was originally trained exclusively on eyes-open data, the network successfully generalized to eyes-closed periods, and this generalization was further improved through task-specific fine-tuning. Finally, fine-tuning also improved decoding of eyelid-state (open versus closed) directly from the MR-signal. These findings demonstrate that gaze and eyelid-state monitoring during eyes-closed periods is feasible, enabling broader integration of eye-tracking in fMRI research.

neuroscience↗

bioRxiv2026

Eye-Tracking-BIDS: the Brain Imaging Data Structure extended to gaze position and pupil data

The Brain Imaging Data Structure (BIDS) is a widely adopted, community-driven standard to organize neuroimaging data and metadata. Although numerous extensions have been developed to incrementally extend coverage to new modalities and data types, an unambiguous, granular specification for eye-tracking recordings is lacking. Here, we present how BIDS will structure data and metadata produced by eye-tracking devices, including gaze position and pupil data. In addition to prescribing the organization of the unprocessed (raw) recordings and associated metadata as produced by the device, BEP20 also resolves gaps in current BIDS specifications beyond the scope of eye tracking. In particular, it adds a mechanism for including asynchronous model parameters and messages, such as contextual information, statuses, and events, such as triggers, generated by the device. BEP20 includes examples that illustrate its applicability in various experimental settings. This BIDS extension provides a robust standard that supports the development of self-adaptive, open, and automated eye-tracking data structures, thereby bolstering transparency and reliability of results in this field.

neuroscience↗