Automatic detection of saccadic eye movements using EOG for analysing effects of cognitive distraction during driving

Abstract

Driver distraction is a relevant driving safety issue and an ongoing field of research. A particular distraction is cognitive distraction, which refers to when the driver is mentally engaged in a task unrelated to driving, e.g. talking to a passenger. Eye movements can be analyzed to study effects of cognitive distraction during driving, and are typically recorded using video-based eye tracker systems. An alternative technique that might be suitable for eye movements measurements during driving is the electro-oculography (EOG). EOG is a method for recording the electrical signal of the eyes as they move. One interesting eye movement in cognitive distraction studies is the saccade, the rapid movement of the eye from one point of interest to another. The primary purpose of this thesis is to develop an algorithm for automatic detection of saccades using EOG. The resulting algorithm is a combination of two modified existing eye detection algorithms, namely Continuous Wavelet Transform Saccade Detection (CWT-SD) and Shape Features. It is found that the developed algorithm can be used in driving environments if good signal quality can be assured. The secondary purpose of this thesis is to investigate how cognitive distraction affects saccadic rate and amplitude during driving. The findings suggest a statistically significant decrease in saccadic rate during cognitive load but not in saccade amplitude. However, further research on bigger datasets and different driving scenarios is needed to verify the results.