Our eyes are in continuous motion. Even when we attempt to fix our gaze, we produce so called “fixational eye movements”, which include microsaccades, drift, and ocular microtremor (OMT). Microsaccades, the largest and fastest type of fixational eye movement, shift the retinal image from several dozen to several hundred photoreceptors and have equivalent physical characteristics to saccades, only on a smaller scale (Martinez-Conde, Otero-Millan & Macknik, 2013). OMT occurs simultaneously with drift and is the smallest of the fixational eye movements (∼1 photoreceptor width, >0.5 arcmin), with dominant frequencies ranging from 70 Hz to 103 Hz (Martinez-Conde, Macknik & Hubel, 2004). Due to OMT’s small amplitude and high frequency, the most accurate and stringent way to record it is the piezoelectric transduction method. Thus, OMT studies are far rarer than those focusing on microsaccades or drift. Here we conducted simultaneous recordings of OMT and microsaccades with a piezoelectric device and a commercial infrared video tracking system. We set out to determine whether OMT could help to restore perceptually faded targets during attempted fixation, and we also wondered whether the piezoelectric sensor could affect the characteristics of microsaccades. Our results showed that microsaccades, but not OMT, counteracted perceptual fading. We moreover found that the piezoelectric sensor affected microsaccades in a complex way, and that the oculomotor system adjusted to the stress brought on by the sensor by adjusting the magnitudes of microsaccades.
Ocular Microtremor (OMT) is a continual, high frequency physiological tremor of the eye present in all subjects even when the eye is apparently at rest. OMT causes a peak to peak displacement of around 150nm-2500nm with a broadband frequency spectrum between 30Hz to 120Hz; with a peak at about 83Hz. OMT carries useful clinical information on depth of consciousness and on some neurological disorders. Nearly all quantitative clinical investigations have been based on OMT measurements using an eye contacting piezoelectric probe which has low clinical acceptability. Laser speckle metrology is a candidate for a high resolution, non-contacting, compact, portable OMT measurement technique. However, tear flow and biospeckle might be expected to interfere with the displacement information carried by the speckle. The paper investigates the properties of the scattered speckle of laser light (λ = 632.8nm) from the eye sclera to assess the feasibility of using speckle techniques to measure OMT such as the speckle correlation. The investigation is carried using a high speed CMOS video camera adequate to capture the high frequency of the tremor. The investigation is supported by studies using an eye movement simulator (a bovine sclera driven by piezoelectric bimorphs). The speckle contrast and the frame to frame spatiotemporal variations are analyzed to determine if the OMT characteristics are detectable within speckle changes induced by the biospeckle or other movements.
Ocular microtremor (OMT) is a physiological high-frequency (up to 150 Hz) low-amplitude (25-2500 nm peak-to-peak) involuntary motion of the human eye. Recent studies suggest a number of clinical applications for OMT that include monitoring the depth of anesthesia of a patient in surgery, prediction of outcome in coma, and diagnosis of brain stem death. Clinical OMT investigations to date have used mechanical piezoelectric probes or piezoelectric strain gauges that have many drawbacks which arise from the fact that the probe is in contact with the eye. We describe the design of a compact noncontact sensing device to measure OMT that addresses some of the above drawbacks. We evaluate the system performance using a calibrated piezoelectric vibrator that simulates OMT signals under conditions that can occur in practice, i.e., wet eye conditions. We also test the device at low light levels well within the eye safety range.
Ocular Microtremor (OMT) is a very fine continuous eye movement which has potential in monitoring and identifying a number of clinical conditions. There is a need for improved analysis and processing techniques to extract useful, quantifiable parameters from the OMT signal. A number of papers have shown the clinical significance of looking at the 'bursts' and 'baseling' patterns of the OMT signal. Analysis to date relies on visual inspection alone. This paper introduces an automated approach to burst/baseline identification based on a time-varying filter using the Gabor transform.
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