New calibration-free gaze tracking system for mice using corneal reflections
Researchers have developed a single-camera, software-only eye tracking system for head-fixed mice that delivers calibrated angular gaze estimates without physical calibration hardware or behavioral tasks. The method uses corneal reflections from multiple fixed LEDs and a geometric model to self-calibrate, eliminating the motorized stages, rotating cameras, or dual-camera setups required by earlier approaches. The system could broaden access to rigorous gaze correction in neuroscience experiments, improving the accuracy of receptive field measurements in studies of visual processing.
A team of researchers has introduced an open-source corneal-reflection eye tracking system for head-fixed mice that requires no per-animal physical calibration procedure. The core innovation is a geometric model that extracts gaze direction in calibrated angular units from the pixel positions of the pupil and reflections of multiple stationary fiducial LEDs, without needing to measure the animal-specific eye-geometry parameter that previous methods required physical calibration to determine. A self-calibration algorithm exploits the redundancy of having several LEDs — each producing an independent gaze estimate — and minimizes disagreement among them to recover a single residual calibration parameter entirely in software. Validation against a rotary-encoder-controlled artificial eye showed mean absolute errors below one degree across the ±20-degree working range typical of mouse eye movements, with Pearson correlations above 0.998 relative to ground truth. The self-calibration reduced inter-LED disagreement by a factor of four to six in live mouse recordings, and applying gaze correction to Neuropixels recordings from mouse primary visual cortex produced noticeably sharper receptive field maps with improved signal-to-noise ratios. The complete pipeline, including pupil and LED detection, 3D geometry setup, blink detection, and artifact correction, is packaged with interactive GUI stages and runs on consumer-grade hardware estimated to cost roughly $2,000–$2,700. The authors argue the multi-LED consistency check also serves as a built-in validation mechanism absent from deep-learning or neural-data-derived correction approaches.
What's missing
The study is a preprint posted to bioRxiv and has not yet undergone formal peer review, so independent replication and expert critique are pending. The validation was performed on a limited number of animals and a single brain region (V1); generalizability to other mouse strains, eye sizes, or experimental configurations (e.g., freely moving animals) is not established. Performance under varying lighting conditions or with non-standard camera hardware has not been systematically characterized.
What different sources said
- bioRxivCenter
Single-camera, calibration-free gaze estimation using corneal reflections
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