HSE Researchers Develop Python Library for Analysing Eye Movements
A research team at HSE University has developed EyeFeatures, a Python library for analysing and modelling eye movement data. This tool is designed to simplify the work of scientists and developers by enabling them to efficiently process complex data and create predictive models.
The project was implemented as part of the Strategic Project 'Human-Centred AI' (Priority 2030).
Modern research increasingly leverages machine learning and artificial intelligence to analyse vast amounts of eye movement data. However, despite significant advancements in this field, certain challenges continue to limit the effectiveness of these methods. One such challenge is the limited flexibility of existing software solutions, which often offer a narrow range of parameter settings, making it difficult to customize them for specific research tasks. Additionally, the integration of these tools with other specialised software remains a significant limitation.
The Python library EyeFeatures, developed by the Laboratory for Social and Cognitive Informatics at HSE Campus in St Petersburg, addresses these challenges by providing a versatile and user-friendly toolkit for working with eye movement data. It includes modules for processing and analysing data collected from eye trackers, devices that monitor eye movement during the performance of various tasks.
Processing eye movement data is a complex task that involves several stages. Since the eyes do not move smoothly but rather in a series of rapid, jerky motions, focusing on specific points, the first stage of data processing is identifying areas of fixation. In the second stage, metrics such as the average gaze fixation duration and the average distance between points are calculated, enabling the creation of initial, simple predictive or diagnostic models.
All stages of data processing can be carried out using the various modules of the EyeFeatures library. The flexible, modular approach makes it easy to integrate eye movement data processing into existing research and commercial projects, from raw data to a fully developed predictive or explanatory model. For example, using the library in marketing research allows for the evaluation of consumer reactions to advertisements. Eye movement analysis will reveal which elements capture the most attention from the audience.
According to Anton Surkov, Project Head, Junior Research Fellow at Laboratory for Social and Cognitive Informatics at HSE Campus in St Petersburg, 'The library can be valuable to researchers, as it enables them not simply to replicate existing functionality from other software but to implement new algorithms and create more advanced models for research in fields such as marketing, cognitive process diagnostics, user interface and neural interface development (where control and interaction with the program occur through eye movement), as well as combine components in innovative ways to achieve new results and enhance methodology.'
This solution streamlines data analysis and accelerates the creation of predictive models, which is particularly beneficial in medical diagnostics, marketing, and the study of cognitive processes. The library has already been applied in research conducted as part of the Strategic Project 'Human-Centred AI' and was presented at the ECEM 2024 international conference in Ireland.
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