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DOE OSTI2024

Evaluation of Saccadic Component Measure on Smooth Pursuit Tests

ABSTRACT Introduction Despite the advancement of eye-tracking technology for smooth pursuit (SP) eye movement evaluation, qualitative observation offers much information that is not captured by computers; hence, both objective and qualitative information should be utilized to evaluate SP. This study examined the consistency among our clinicians when evaluating SP using normal (N), grossly normal (GN), mildly abnormal (MA), and abnormal (AB) as classifications. We then evaluated the effect of combining GN and MA into a single subclinical (SUBC) category. We also evaluated the computerized percent saccade (PS) metric by determining its sensitivity and specificity in classifying SP. Materials and Methods Retrospective horizontal and vertical SP test videos and numerical data for 70 participants were obtained from the Neuro Kinetics Neuro-Otologic Test Center and de-identified. From this, eye-tracking videos, time plots of eye-tracking positional data, and tables of SP eye-tracking performance data were generated for 0.1, 0.3, and 0.5 Hz in both horizontal and vertical planes, totaling 6 tests per subject. Three clinicians rated each subject’s SP performance as N, GN, MA, or AB for a total of 6 ratings (3 frequencies, horizontal and vertical). This process was repeated using N, SUBC, and AB as rating categories. Clinicians also provided an overall SP rating for each plane as follows: AB if the results were abnormal for 2 or more frequencies tested. Alternatively, if fewer than 2 frequencies presented with a rating of AB, then an overall rating of MA, GN, or N was determined at the respective clinician’s discretion. Results When the 3 clinicians were tasked with classifying SP videos using 4 clinical categories, fair overall agreement was demonstrated. However, when MA and GN categories were combined into an SUBC category, the overall agreement for the 3 clinicians improved slightly for both horizontal SP (HSP) and vertical SP (VSP). This pattern of agreement did not differ considerably when comparing HSP versus VSP, and good consistency and reliability was observed across clinicians. Again, inter-rater consistency was smaller for VSP versus HSP despite the reduction in clinical categories. Cut-off values were generated for the PS metric and demonstrated good specificity and sensitivity when they were exceeded for 2 or more frequencies in a particular plane when evaluating a subject’s SP test. Conclusions

General & Internal Medicine↗

DOE OSTI2018

Unsupervised Parsing of Gaze Data with a Beta-process Vector Auto-regressive Hidden Markov Model

The first stage of analyzing eye-tracking data is commonly to code the data into sequences of fixations and saccades. This process is usually automated using simple, predetermined rules for classifying ranges of the time series into events, such as “if the dispersion of gaze samples is lower than the threshold, then code as a fixation; otherwise code as a saccade.” More recent approaches incorporate additional eye-movement categories in automated parsing algorithms, particularly glissades, by using time-varying, data-driven thresholds. We describe an alternative approach using the beta-process auto-regressive hidden Markov model (BP-AR-HMM). The BP-AR-HMM offers two main advantages over existing frameworks. First, it provides a statistical model for eye movement classification rather than a single estimate. Second, the BP-AR-HMM uses a latent process to model the number and nature of the types of eye-movements and hence is not constrained to predetermined categories. We applied the BP-AR-HMM both to high-sampling rate gaze data from Andersson, Larsson, Holmqvist, Stridh, and Nyström (2016) and to low-sampling rate data from the DIEM project (Mital, Smith, Hill, & Henderson, 2011). Driven by the data properties, the BP-AR-HMM identified clusters of samples resembling saccades, fixations and smooth pursuit, and a category that approximately corresponds to glissades (as well as blinks, errors, etc.). The BP-AR-HMM serves as an effective algorithm for data-driven event parsing alone or as an initial step in exploring the characteristics of gaze data sets.

eye tracking, fixation, fixation, saccade, smooth ↗

DOE OSTI2024

Normative Ranges for Oculomotor and Reaction Time Tests in U.S. Military Service Members and Veterans

AbstractBackground Oculomotor and reaction time tests are frequently used assessments of vestibular symptoms, traumatic brain injury (TBI), or other neurological disorders in both clinical and research contexts. When interpreting these tests it is important to have a reference interval (RI) as a comparison for what constitutes a typical/expected response; however, the current body of research has only limited information regarding normative ranges calculated according to established standards or for a military-specific sample.Purpose The purpose of the present study was to describe RIs for oculomotor and reaction time tests in a cohort of service members and veterans (SMVs) for use as comparators by clinicians and scientists.Research Design Descriptive.Study Sample Participants were prospectively enrolled in the Defense and Veterans Brain Injury Center-Traumatic Brain Injury Center of Excellence 15-year Longitudinal Traumatic Brain Injury Study. Only SMVs without a history of TBI or blast exposure were included in the RI calculations.Data Collection and Analysis The test paradigms included in this analysis were: smooth pursuit, prosaccades, antisaccades, saccades and reaction time, predictive saccades, optokinetic nystagmus, auditory reaction time, and visual reaction time. Nonparametric methods, based on the U.S. Food and Drug Administration's recognized consensus standards, were used to calculate 95% RIs. A comparison between the calculated RIs and those available from previously published research is provided.Results Summary statistics and RIs were calculated for 47 outcome parameters from 13 oculomotor and reaction time tests. Sample sizes and age ranges varied across outcome parameters depending on the availability of reference values for RI calculations. The sample sizes used to calculate RIs ranged from 51 to 69. The age of SMVs included in each RI ranged from 19 to 61 years with mean ages ranging from 37 to 39 years. Similarities/differences between the RIs in the present study and those in previously published research are highly dependent on the outcome parameter; however, in general, the RIs in the present study tended to be somewhat wider.Conclusion The RIs provided in this paper can serve as comparisons for clinicians and scientists who are utilizing these oculomotor and reaction time testing paradigms in similar cohorts of patients or research participants.

Audiology & Speech-Language Pathology↗

DOE OSTI2024

Temporal light modulation: A phantom array visibility measure

At temporal light modulation (TLM) frequencies between 80 Hz and 20 000 Hz observers may perceive a series of repeated images called the phantom array effect (PAE) when they move their eyes in large saccades across a modulating light source or across a scene lit by the modulating light source. To date, there is no well-established measure for quantifying PAE visibility, but there is growing awareness of the need for one among design professionals and sensitive populations. This paper documents a new measure, the phantom array visibility measure (PAVM), which is based on the results of recent human factors experiments. The measure follows the mathematical underpinning used by the flicker visibility measure and the stroboscopic visibility measure, where the time-domain TLM waveform is converted into its Fourier frequency components; each component is evaluated through a threshold curve of modulation depth, then summed through an equation employing a Minkowski exponent. This scales the PAVM so that a value of 1 indicates a waveform at a threshold visibility in the conditions of the underlying experiment.

Tan, J.↗

DOE OSTI2024

Visibility and annoyance of the phantom array effect varies with age and history of migraine

The phantom array effect (PAE) is a series of repeated images that may be perceived when a person moves their eyes in large saccades across a light source (or a specular reflection of that light source) that is modulating in output over time. Fifty-five people, including a group of 25 who experience migraine, evaluated the visibility and annoyingness of phantom arrays produced by 85 unique temporal light modulation waveforms (including sine, rectangular, complex and DC waveforms) generated using an LED placed against a black background. Those with migraine exhibited higher average visibility compared to those without migraine ( p = 0.019) and were relatively more sensitive at higher frequencies ( p < 0.001). Younger participants also found more stimuli to be visible ( p < 0.001). The threshold sensitivity function was similar to that developed for the phantom array visibility measure (PAVM), and PAVM was effective in predicting visibility ( R 2 = 0.87 for the relevant region of PAVM < 3). While those in the migraine group did not report seeing the PAE more often in everyday life at a statistically significant level, they reported being more annoyed by it and having more unwanted physiological responses (headaches, eye fatigue and distraction/disorientation). Members of the migraine group were also more likely to have changed their behaviour in architectural spaces (such as leaving a restaurant with ‘flickering’ lights). In the four hours after completing the experiment, 64% of the migraine group (vs. 19% of the non-migraine group) reported experiencing discomfort or an adverse reaction. In particular, 41% reported experiencing a headache (vs. 8% for those in the non-migraine group).

60 APPLIED LIFE SCIENCES↗