## 1 Introduction  
Eye tracking is highly interdisciplinary and can be used in numerous fields, including cognitive science, psychology, neurology, engineering, medicine, and marketing [1, 2]. The application of eye tracking in human-computer interaction allows disabled people to interact with a computer through gaze [3]. Eye monitoring can also be used to track car drivers [4] or pilots [5]. Extracting events from eye movement data is the most important step for automated visual behavior analysis and for many applications [6]. The movement of the eye is typically divided into several types of events. The most important is fixation, a low-speed movement that occurs when the person focuses their eyes on an object. The purpose of fixation is to stabilize the object in the fovea for clear vision [7]. Saccade is a high-speed eye movement from one fixation to another [8]. A smooth pursuit (SP) event is performed only when the eyes follow a slowly moving object [9, 10]. Post-saccadic oscillation (PSO) events are rapid oscillatory movements that may occur immediately after a saccade. They can be described as instabilities that appear at the end of a saccade and are characterized by a slight wobbling movement that leads to fixation after a saccade [10; 11]. The problem of automatic detection of eye movement events has been studied for many years. However, it is associated with many challenges. For instance, raw eye-tracking data frequently consist of disturbances and noise, which may make it difficult for detection methods to identify events correctly. Moreover, differentiating between PSOs, SPs, fixations, and saccades, as well as finding precise onsets and offsets of events, may be difficult even for manual coders, and there is evidence that there are differences in event classification even between experienced coders [12; 13]. Today, event detection is performed by processing raw eye-tracking data with specialized detection algorithms. Threshold-based and fully automated event detection algorithms have been used in previous works. For a long time, two broad classes of threshold-based algorithms have been used to detect eye movement events. The first class includes dispersion threshold-based algorithms that detect fixations [14]. The most popular dispersion-based algorithm is the I-DT algorithm by [PERSON] and [PERSON] [15]. These algorithms detect events, assuming that, for the fixation, the raw recorded data must fit in a spatial box for a specified minimum time. The second category is the velocity-based algorithms that focus on detecting saccades. The most popular velocity-based algorithm is the I-VT algorithm [16; 17]. These algorithms classify eye movement data by calculating velocities and comparing them with a predefined threshold. Although the algorithms above differentiate between fixations and saccades only, [PERSON] et al. [18] proposed the first automated event detection method that may also be used to classify smooth pursuits. [PERSON] et al. [19] proposed another threshold-based approach for identifying fixations and smooth pursuits during intersaccadic intervals. The approach performed the classifications in three stages, as follows: a preliminary segmentation evaluating the characteristics of each such segment and reorganizing the preliminary segments into fixations and smooth pursuit events. This approach was proposed to address the misclassification between fixations and smooth pursuits. However, like all other threshold-based approaches, this approach also requires the end users to set threshold parameters manually, and the three-stage identification process makes the approach complex and time-consuming. As event detection involves classifying each raw gaze sample into a separate class (event), applying machine learning algorithms to this task seems straightforward. Several attempts have been published using machine learning and, specifically, deep learning techniques to perform event detection [20; 21; 22]. Details are provided in the Section 2.