Identification of Ambient and Focal Information Processing Phases Using Eye Movement Response Registration

In this study, we conduct research aimed at differentiating types of attention during image inspection. In our investigation, we utilized the EyeLink 1000 Plus eye tracker and machine learning methods to identify phases of ambient and focal information processing. A series of experiments were conducted with 10 participants who examined images of rooms with interior items. Data on the trajectory of the gaze point was recorded using the EyeLink 1000 Plus eye tracker. We developed methods for processing eye tracker data for analyzing eye movement in the task of free inspection. These methods include applying the fixation segmentation method by areas of interest, speed, and duration. We also developed an eye tracker data classification algorithm to identify ambient and focal types of attention. The results showed that the ambient type of attention is characterized by high speed, short eye fixations, and is not tied to a specific object in space, while the focal type of attention is characterized by prolonged eye fixations and is tied to specific significant objects. These results can be used to develop innovative Brain-Computer Interface (BCI) and Eye-Brain-Computer Interface (EBCI), opening up new opportunities for research in neuroscience and user interface development.