Application of machine learning methods for saccades and fixation detection from eye-tracking signal.

The aim of the study is to investigate the possibility of applying machine learning methods for saccades and fixation detection from eye tracking signals. There are two widely used approaches to solving this task: velocity-based saccade identification (I-VT) and dispersion-based saccade identification (I-DT). Both require setting an optimal threshold which directly influences detection quality. Another important problem comes from the necessity of noise handling if eye tracking signal has a high noise level. Using I-VT and I-DT algorithms in this case will result in misclassification. Previous researchers have obtained promising results using Random Forest algorithm for saccade and fixation detection. Our aim with the article was to compare Random Forest with other binary classification algorithms such as Logistic Regression and K-Nearest Neighbors. We've also compared mentioned algorithms with velocity-based approach. The results show that Random Forest has the best classification metrics. The findings suggest that this machine learning approach could also be useful for eye movement event detection and proves the importance of further research in this field [ABSTRACT FROM AUTHOR]