Abstract

In this study, we analyzed the blinking behavior of players in a video game tournament. Our aim was to test whether spontaneous blink patterns differ across levels of expertise. We used blink rate, blink duration, blink frequency, and eyelid movements represented by the Eye Aspect Ratio (EAR) to train a machine learning classifier to discriminate between different levels of expertise. Classifier performance was highly influenced by features such as the mean, standard deviation and median EAR. Moreover, further analysis suggests that blinking rate and blink duration are likely to increase along with the level of expertise. We speculate this may be indicative of a reduction in cognitive load and lowered stress of expert players. In general, our results suggest that EAR and blink patterns can be used to identify different levels of expertise of video game players.