Data and Software for paper: Eye-tracking with accessible technology and open-source software: a validity study using commercial eye-trackers

Objective. To determine the diagnostic potential of readily quantifiable metrics of eye- movement, captured using an off-the-shelf-camera. This study investigated whether a difference between two subject groups: control and concussed, could be detected by eye-tracking, and whether a webcam and open-source software could produce comparable results with commercial eye-tracking systems. Methods. The study included 9 control and 9 concussed subjects, who performed a saccadic eye-movement task, while being recorded on a webcam and a commercial eye-tracker. The open-source Tracker software was used to digitize the eye movement and measure the x and y displacement of the eye. Analysis code analyzed eye tracking data to identify and characterize saccadic eye movements. Metrics of interest included the time constant (τ [ms]), and the fit before, during, and after each transition. Results. There was a significant difference in all parameters between the control and concussed subjects, indicating that concussed subjects had slower transition time and difficulty fixating on a target. No significant differences were found between the calculated metrics from the webcam and the commercial eye-trackers, which validated that our method was able to accurately quantify eye movements. Conclusions. This study supported the use of an accessible eye tracking and open-source software approach. A novel finding of this study was that there were significantly higher parameters in concussed subjects during saccadic eye movement.

The Test Protocol- Calibration.pptx is the stimulus file that was shown to the subjects. 

The fit-eye-parameters5.py is the python code that was used to analyze the saccadic eye movements.

The Eye-tracking subject 3,6.xlsx is an excel file with an example eye-tracking trace for a control and concussed subject.