## 7 Conclusions and Future Works  
A deep learning method was proposed to extract eye movement events from raw eye-tracking data. The main objective of this study was to identify the best feature sets for building a model that simultaneously classifies four event types. The velocity, acceleration, jerk, and direction characteristics were used. Combinations of these different features were used to detect events. The classification performance of the proposed model across different feature combinations was compared with that of the state-of-the-art model. The results revealed that combining velocity and acceleration with either direction, jerk, or both jerk and acceleration features significantly improved performance compared to other feature sets. This indicated that the VAJD, VAD, and VJD feature combinations outperformed the AJD, VD, and AD feature combinations. Furthermore, we analyzed the impact of each feature on the detection of specific events, i.e., fixations, saccades, PSOs, and smooth pursuits. To the best of our knowledge, this is the first fully automated event detection method that simultaneously classifies fixations, saccades, PSOs, and smooth pursuits. The event measures for the proposed methods were also evaluated and compared with the manual classification results. The event-measuring metrics included the number of events and the mean duration of events. Obviously, the work carries certain limitations. Our results, even for the best-performing models, are far from perfect, and there is room for improvement. In particular, the accuracy of the PSO classification is relatively low. In this paper, we focus on the analysis of the best combinations of different features. The next step should be to use the best-performing feature combinations to build more robust models. Future work will focus on extending the research through an extensive analysis of various network architectures and hyperparameters. Furthermore, more data will be used for training to further improve the model's performance. As stated in the performance evaluation section, comparing different algorithms with different conditions and objectives is difficult [33]. Therefore, we were only able to compare the proposed models with the one that was the most similar to ours. However, this model originally did not consider PSO events, so the comparison was not entirely fair. No model was found that automatically classifies all four events. In addition, it would be valuable to test the performance of the algorithms using other datasets. Specifically, this work focuses on video stimulus datasets labeled with fixations, saccades, PSOs, and smooth pursuits. Therefore, testing the models on other types of data could tell more about the generality of our methods. Since PSOs are the most problematic, future work will analyze how they differ across different types of stimuli and attempt to develop models tailored to specific types. The other line of enhancement is based on improving the accuracy of the PSO classification in the presence of fixations, saccades, and smooth pursuits. The proposed method improves existing methods by simultaneously classifying fixations, saccades, PSOs, and SPs. However, we believe that the proposed approach can be further optimized in such a way that the accuracy of the PSO is taken into account. conceptualization, P.K. ; software, B.A.B. ; calculations, B.A.B., data curation, B.A.B., P.K. ; writing--original draft preparation, B.A.B. ; writing--review and editing, P.K. ; supervision, P.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the Silesian University of Technology, Poland grant number BKM/572/RAU7/2024. Ethical review and approval were waived for this study because no experiments on humans or animals were done. Not applicable.