This video showcases Merlin’s Auto-Focus and Auto-Brightness features in action. At the start, a range of focal depths and brightness levels are tested through single-frame captures. These images are then analyzed by Merlin’s algorithm to determine the optimal focus and illumination settings.

Once computed, the system automatically applies these values—ensuring clear, well-lit imagery without manual adjustment.

Should the algorithm be unable to identify an ideal setting, or if you wish to prioritize a specific region of interest, you can easily adjust focus and brightness manually using intuitive slider controls.

Nystagmus refers to involuntary, rhythmic eye movements—often described as tremors of the eye. These oscillations can be physiological or pathological and are commonly observed in cases of vertigo.

In this video, the Merlin system tracks the pupil center, shown as a green dot. The corresponding horizontal (X-axis) pupil position is plotted as a blue line, converted into degrees of visual angle.

Throughout the recording, you’ll observe recurring patterns of slow and fast eye movements—hallmarks of jerk nystagmus. These rhythmic phases are captured with high temporal precision, demonstrating Merlin’s capability to detect and analyze subtle oculomotor activity in real time.

The blue line in the diagram represents horizontal (X-axis) pupil position. An upward trend indicates the eye is moving rightward, while a downward trend reflects a leftward movement.
Red bars mark the detected nystagmus peaks. The movement preceding each peak is known as the slow phase, and the movement following is the fast phase—together forming the characteristic jerk nystagmus waveform.

This video shows a live eye recording captured with the Merlin system. The pupil center is tracked and visualized as a green dot. When the dot turns red, it indicates that the algorithm could not confidently detect the pupil—typically due to obstructions like eyelashes or a blink.

Our detection algorithm uses a multiscale ring filter bank to analyze the eye image. From the filter responses, it selects the most likely pupil center and calculates a confidence score for each frame.

This score not only guides real-time tracking accuracy but also supports blink detection and post-processing quality control—giving researchers a reliable signal they can trust in complex scenarios.