In this paper, we present a new feature that can be used to classify cognitive load based on pupil information. The feature consists of a temporal segmentation of the eye tracking recordings. For each segment of the temporal partition, a probability distribution of pupil size is computed and stored. These probability distributions can then be used to classify the cognitive load. The presented feature significantly improves the classification accuracy of the cognitive load compared to other statistical values obtained from eye tracking data, which represent the state of the art in this field. The applications of determining Cognitive Load from pupil data are numerous and could lead, for example, to pre-warning systems for burnouts.

In this paper, we evaluate different small neural network models for eye movement classification and show our so far developed improved model architecture. For evaluation, we used a subset (1.5 million sequences) of the TEyeDS annotations since it contains in the wild recordings and has the most eye movement annotations to our knowledge. We classified fixations, saccades, and smooth pursuits with four different network architectures and the proposed model improves the equally weighted accuracy by 3.8% to the best competitor while only using 6% of the amount of learnable weights.

In this paper, we present two approaches and algorithms that adapt areas of interest (AOI) or regions of interest (ROI), respectively, to the eye tracking data quality and classification task. The first approach uses feature importance in a greedy way and grows or shrinks AOIs in all directions. The second approach is an extension of the first approach, which divides the AOIs into areas and calculates a direction of growth, i.e. a gradient. Both approaches improve the classification results considerably in the case of generalized AOIs, but can also be used for qualitative analysis. In qualitative analysis, the algorithms presented allow the AOIs to be adapted to the data, which means that errors and inaccuracies in eye tracking data can be better compensated for. A good application example is abstract art, where manual AOIs annotation is hardly possible, and data-driven approaches are mainly used for initial AOIs.

In this paper, we present two approaches and algorithms that adapt areas of interest. We present a new deep neural network (DNN) that can be used to directly determine gaze position using EEG data. EEG-based eye tracking is a new and difficult research topic in the field of eye tracking, but it provides an alternative to image-based eye tracking with an input data set comparable to conventional image processing. The presented DNN exploits spatial dependencies of the EEG signal and uses convolutions similar to spatial filtering, which is used for preprocessing EEG signals. By this, we improve the direct gaze determination from the EEG signal compared to the state of the art by 3.5 cm MAE (Mean absolute error), but unfortunately still do not achieve a directly applicable system, since the inaccuracy is still significantly higher compared to image-based eye trackers.