A novel blink detection method based on pupillometry noise.

Pupillometry (or the measurement of pupil size) is commonly used as an index of cognitive load and arousal. Pupil size data are recorded using eyetracking devices that provide an output containing pupil size at various points in time. During blinks the eyetracking device loses track of the pupil, resulting in missing values in the output file. The missing-sample time window is preceded and followed by a sharp change in the recorded pupil size, due to the opening and closing of the eyelids. This eyelid signal can create artificial effects if it is not removed from the data. Thus, accurate detection of the onset and the offset of blinks is necessary for pupil size analysis. Although there are several approaches to detecting and removing blinks from the data, most of these approaches do not remove the eyelid signal or can result in a relatively large amount of data loss. The present work suggests a novel blink detection algorithm based on the fluctuations that characterize pupil data. These fluctuations ("noise") result from measurement error produced by the eyetracker device. Our algorithm finds the onset and offset of the blinks on the basis of this fluctuation pattern and its distinctiveness from the eyelid signal. By comparing our algorithm to three other common blink detection methods and to results from two independent human raters, we demonstrate the effectiveness of our algorithm in detecting blink onset and offset. The algorithm's code and example files for processing multiple eye blinks are freely available for download ( https://osf.io/jyz43 ).