• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

HSE Researchers Develop Python Library for Analysing Eye Movements

HSE Researchers Develop Python Library for Analysing Eye Movements

© iStock

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.

See also:

From Neural Networks to Stock Markets: Advancing Computer Science Research at HSE University in Nizhny Novgorod

The International Laboratory of Algorithms and Technologies for Network Analysis (LATNA), established in 2011 at HSE University in Nizhny Novgorod, conducts a wide range of fundamental and applied research, including joint projects with large companies: Sberbank, Yandex, and other leaders of the IT industry. The methods developed by the university's researchers not only enrich science, but also make it possible to improve the work of transport companies and conduct medical and genetic research more successfully. HSE News Service discussed work of the laboratory with its head, Professor Valery Kalyagin.

Children with Autism Process Sounds Differently

For the first time, an international team of researchers—including scientists from the HSE Centre for Language and Brain—combined magnetoencephalography and morphometric analysis in a single experiment to study children with Autism Spectrum Disorder (ASD). The study found that children with autism have more difficulty filtering and processing sounds, particularly in the brain region typically responsible for language comprehension. The study has been published in Cerebral Cortex.

HSE Scientists Discover Method to Convert CO₂ into Fuel Without Expensive Reagents

Researchers at HSE MIEM, in collaboration with Chinese scientists, have developed a catalyst that efficiently converts CO₂ into formic acid. Thanks to carbon coating, it remains stable in acidic environments and functions with minimal potassium, contrary to previous beliefs that high concentrations were necessary. This could lower the cost of CO₂ processing and simplify its industrial application—eg in producing fuel for environmentally friendly transportation. The study has been published in Nature Communications. 

HSE Scientists Reveal How Staying at Alma Mater Can Affect Early-Career Researchers

Many early-career scientists continue their academic careers at the same university where they studied, a practice known as academic inbreeding. A researcher at the HSE Institute of Education analysed the impact of academic inbreeding on publication activity in the natural sciences and mathematics. The study found that the impact is ambiguous and depends on various factors, including the university's geographical location, its financial resources, and the state of the regional academic employment market. A paper with the study findings has been published in Research Policy.

Group and Shuffle: Researchers at HSE University and AIRI Accelerate Neural Network Fine-Tuning

Researchers at HSE University and the AIRI Institute have proposed a method for quickly fine-tuning neural networks. Their approach involves processing data in groups and then optimally shuffling these groups to improve their interactions. The method outperforms alternatives in image generation and analysis, as well as in fine-tuning text models, all while requiring less memory and training time. The results have been presented at the NeurIPS 2024 Conference.

When Thoughts Become Movement: How Brain–Computer Interfaces Are Transforming Medicine and Daily Life

At the dawn of the 21st century, humans are increasingly becoming not just observers, but active participants in the technological revolution. Among the breakthroughs with the potential to change the lives of millions, brain–computer interfaces (BCIs)—systems that connect the brain to external devices—hold a special place. These technologies were the focal point of the spring International School ‘A New Generation of Neurointerfaces,’ which took place at HSE University.

New Clustering Method Simplifies Analysis of Large Data Sets

Researchers from HSE University and the Institute of Control Sciences of the Russian Academy of Sciences have proposed a new method of data analysis: tunnel clustering. It allows for the rapid identification of groups of similar objects and requires fewer computational resources than traditional methods. Depending on the data configuration, the algorithm can operate dozens of times faster than its counterparts. Thestudy was published in the journal Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia.

Researchers from HSE University in Perm Teach AI to Analyse Figure Skating

Researchers from HSE University in Perm have developed NeuroSkate, a neural network that identifies the movements of skaters on video and determines the correctness of the elements performed. The algorithm has already demonstrated success with the basic elements, and further development of the model will improve its accuracy in identifying complex jumps. 

Script Differences Hinder Language Switching in Bilinguals

Researchers at the HSE Centre for Language and Brain used eye-tracking to examine how bilinguals switch between languages in response to context shifts. Script differences were found to slow down this process. When letters appear unfamiliar—such as the Latin alphabet in a Russian-language text—the brain does not immediately switch to the other language, even when the person is aware they are in a bilingual setting. The article has been published in Bilingualism: Language and Cognition.

HSE Experts Highlight Factors Influencing EV Market Growth

According to estimates from HSE University, Moscow leads in the number of charging stations for electric vehicles in Russia, while Nizhny Novgorod ranks first in terms of charging station coverage, with 11.23 electric vehicles per charging station, compared to 14.41 in Moscow. The lack of charging infrastructure is one of the key factors limiting the growth of the electric vehicle market. This is stated in the study titled ‘Socio-Economic Aspects of Introducing Electric Vehicles in Commercial Transportation’ conducted by experts from the Institute of Transport Economics and Transport Policy Studies at HSE University.