## 4 Proposed Method
### Performance Evaluation  
Currently used fully automated event detection algorithms cannot simultaneously detect all four events (i.e., fixations, saccades, PSOs, and smooth pursuits). Additionally, there is no standard performance metric for eye movement event detectors [33]. So, for a fair comparison, it is recommended that they be compared using the same metrics (either against other algorithms or against the same algorithm under different conditions), using an applicable evaluation method [33].  
Threshold-based methods were not considered for comparison, as they are statically designed for specific event types based on the threshold value. For example, I-DT and I-VT are designed to detect fixation and saccades only. However, the dataset used for evaluation is annotated with four different event types. Therefore, comparing our method with threshold-based methods would be unfair.  
The method was compared with the one most similar to ours, as presented in [21]. This method also used a neural network built from a combination of CNN and LSTM layers, but they used 1D-CNN and BLSTM layers. Another difference was that their model output a sequence of event points, while ours classified only one point at a time. In addition, their method was designed to identify only fixations, saccades, and smooth pursuits. It did not consider PSOs, which were tricky to distinguish from fixations, saccades, and smooth pursuits due to their behavioral overlap with these event types.  
To compare their model with ours, the model was reimplemented, and the final layer was changed to return only one value, as in ours. In addition, a 50-sample window was  
Figure 2: Calculation of features.  
used as input to the model. The model was then trained on the same data as used in our experiments.  
However, the primary purpose was to compare the same model for different feature sets. This comparison was made on the following two levels: sample-level and event-level. For the sample level, the output of the trained model was compared with the ground truth, and classic measures such as the F1 score and Cohen's kappa were calculated for each sample. Furthermore, the corresponding confusion matrices for each combination of features--i.e., velocity (V), acceleration (A), direction (D), and jerk (J)--were analyzed.  
Classification of single-gaze data points is just the first step in event detection [9]. The next step is to join identically classified neighboring points to form eye movement events. The process is visualized in Figure 3. There are many possible measures to compare such results. However, there is no commonly accepted measure for this event-level comparison [33]. Therefore, we compared general event statistics, i.e., the number of events and their mean duration. We believe that such information gives a reliable way to compare different results.  
The train and test dataset split was done using the leave-one-file-out (LOFO) cross-validation method to avoid biased and overestimated model performance using a single test set, because we repeatedly fit a model to a dataset containing n-1 files. In our case, six files from the dataset described in Section 3 were used.