## 2 Related Works  
Event detection can be performed manually by human coders or algorithms. In manual event detection, one or more human experts classify raw eye-tracking data into different event types based on subjective threshold values. Manual event classification is still treated as a \"golden standard\" [12]. However, manual classification is ineffective at classifying eye movement events. Firstly, it is time-consuming, and secondly, different coders might also use distinctive subjective selection regulations that give different results [23]. Threshold-based methods were the first automated eye movement event classification algorithms and are still frequently used [15]. For example, [PERSON] and [PERSON] evaluated five different threshold-based event detection algorithms that were introduced by different authors. The evaluation was based on spatial and temporal criteria. The algorithms were namely, I-VT, I-DT [15], I-HMM [24], I-AOI, and I-MST [15]. The I-DT is a simple eye movement event detection algorithm that distinguishes fixation and saccade points based on the dispersion of subsequent sample coordinates. The algorithm classifies gaze samples as elements of fixation when they are located within a spatially limited area (for example, 0.5\({}^{\circ}\)) for the minimum allowed fixation duration [25]. It follows the assumption that fixation points generally occur close to each other. The saccades are then implicitly classified as everything else [15]. However, dispersion threshold methods poorly detect fixations and saccades when the signal is noisy [26]. Therefore, choosing the optimum threshold greatly impacts the classification performance and can lead to biased results and misclassifications. The I-VT event detection algorithm is another threshold-based algorithm and the foundation for automated, objective standard event detection. Many studies have adopted this approach [27; 28]. It assumes that saccadic eye movements are characterized by high velocity, and fixational movements by low velocity [15]. However, just like I-DT, the classic I-VT method is designed to classify eye-tracking input data into fixations and saccades only. The other event types, like smooth pursuits, post-saccadic oscillations, and noises, are not considered. Finding the optimum threshold value is also challenging. One of the main problems with threshold-based event detection methods discussed in this section is that they can only identify fixation and saccade movements. In order to address this problem, [PERSON] and [PERSON] [18] proposed the first ternary automated event detection methods. The methods classify raw eye-tracking data into fixations,saccades, and smooth pursuits. The methods are I-VVT, I-VDT, and I-VMP. The I-VVT algorithm identifies fixations, saccades, and smooth pursuits (SPs) by first applying the I-VT algorithm to classify fixations and saccades using the existing threshold, then using one more threshold to distinguish SPs from fixations. The I-VMP algorithm distinguishes smooth pursuits from fixations using the movement direction. The I-VDT algorithm integrates both I-VT and I-DT to classify fixations, saccades, and smooth pursuits. As in I-VVT, I-VDT first applies the velocity threshold to classify saccades and fixations, and then the dispersion threshold is applied to distinguish fixations and smooth pursuits. The drawback of these methods is that they are still threshold-based, so the user must manually set the optimum threshold values. In addition, the identification process is done in two steps and requires two optimum threshold values. Finding the optimum threshold value in threshold-based event detection methods is challenging, as threshold values vary depending on the type of task, data quality, and the user. In order to address these issues, [PERSON]. [29] proposed an automated velocity threshold data-driven event classification method. The algorithm can find the threshold adaptively and avoid the influence of noise by using adaptive VT values, considering different levels of noise occurrence.