## 5 Results and Discussion  
In this section, the results and discussions on the impacts of various feature combinations on the classification performances of different eye movement events are presented. Direction plays a crucial role in identifying smooth pursuit (SP), as it captures the change in the direction of eye movement, which is key to distinguishing SP from other event types. Velocity is essential for distinguishing saccades from fixations and smooth pursuits, as it provides a clear differentiation between high-speed and low-speed movements. Additionally, acceleration and jerk contribute significantly to detecting post-saccadic oscillations (PSOs), as they help to separate PSOs from other movement types based on their rapid, oscillatory nature. Our analysis shows that combinations of velocity, acceleration, jerk, and direction (such as VAJD, VAD, and VJD) improve classification performance, particularly for PSO detection. We also found that while the absence of jerk in the VAD combination did not significantly affect performance, the lack of velocity in the AJD combination severely impacted the model's ability to correctly classify events. The performances of different models are presented in Tables 2 and 3. The models using different feature combinations were compared with the ground truth (human coders) as a reference. The baseline model with velocity and direction features, originally used in [21], was also evaluated for fixation, saccades, and smooth pursuit classification. Precision and F1 score were calculated for the proposed models. In order to measure the overall agreement between the manual coders and the proposed methods, [PERSON]'s kappa was  
Figure 3: Merging neighboring events. F, S, PSO, and SP stand for fixation, saccade, post-saccadic oscillation, and smooth pursuit, respectively. calculated between each manual coder and the proposed methods. Moreover, confusion matrix analysis was used to analyze the performance of the models. The experiment started with the combinations of VD and AD feature combinations, which were used in the baseline method [21]. It was discovered that the AD feature combination exhibited very low event detection performance due to its inability to distinguish fixations from smooth pursuits and PSOs from all other event types. A combination of velocity and direction (VD) can identify saccades and PSOs, as velocity can identify saccades from fixations and smooth pursuits. However, the VD combination performs poorly in identifying PSOs due to their speed-related behavior being similar to that of saccades. The results show that using acceleration or jerk in addition to velocity and direction helps the model to distinguish PSOs from other event types. This implies that the model's classification performance with the VAJD, VAD, and VJD feature combinations is superior to that of the AJD, VD, and AD combinations. Not surprisingly, the AJD feature combination cannot classify all event types, especially PSOs. This implies that combining velocity and direction with acceleration and/or jerk shows significant performance improvement for all event types, including PSO. As the results show, feature selection significantly impacts the event classification results. On the one hand, velocity is crucial for correctly detecting saccades from fixations and smooth pursuits. Hence, these events have low scores for AD and AJD sets. On the other hand, velocity is not sufficient to correctly distinguish between fixations and SPs, as