eye-movement data

The electrooculography signal is widely used to analyze eye movements, with an emphasis on its use in human-computer interaction. Various techniques based on artificial intelligence have been used for signal processing. These methods require a specific dataset to train algorithms capable of detecting eye movements. We designed an experiment in which horizontal and vertical eye movements were recorded in conjunction with different movement angles.


Pressing demand of workload along with social media interaction leads to diminished alertness during work hours. Researchers attempted to measure alertness level from various cues like EEG, EOG, Video-based eye movement analysis, etc. Among these, video-based eyelid and iris motion tracking gained much attention in recent years. However, most of these implementations are tested on video data of subjects without spectacles. These videos do not pose a challenge for eye detection and tracking.


We present a dataset of human visual attention on 2D images during scene free viewing. This dataset includes 1900 images, which are corrputed by various image transformations. This dataset is manually annotated with human eye-movement data recorded by Tobii X120 eye-tracker. This dataset provides a new benchmark to measure the robustness of saliency prediction models on various transformed scenes.