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.
See also:
Machine Learning Links Two New Genes to Ischemic Stroke
A team of scientists from HSE University and the Kurchatov Institute used machine learning methods to investigate genetic predisposition to stroke. Their analysis of the genomes of over 5,000 people identified 131 genes linked to the risk of ischemic stroke. For two of these genes, the association was found for the first time. The paper has been published in PeerJ Computer Science.
First Digital Adult Reading Test Available on RuStore
HSE University's Centre for Language and Brain has developed the first standardised tool for assessing Russian reading skills in adults—the LexiMetr-A test. The test is now available digitally on the RuStore platform. This application allows for a quick and effective diagnosis of reading disorders, including dyslexia, in people aged 18 and older.
Low-Carbon Exports Reduce CO2 Emissions
Researchers at the HSE Faculty of Economic Sciences and the Federal Research Centre of Coal and Coal Chemistry have found that exporting low-carbon goods contributes to a better environment in Russian regions and helps them reduce greenhouse gas emissions. The study results have been published in R-Economy.
Russian Scientists Assess Dangers of Internal Waves During Underwater Volcanic Eruptions
Mathematicians at HSE University in Nizhny Novgorod and the A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences studied internal waves generated in the ocean after the explosive eruption of an underwater volcano. The researchers calculated how the waves vary depending on ocean depth and the radius of the explosion source. It turns out that the strongest wave in the first group does not arrive immediately, but after a significant delay. This data can help predict the consequences of eruptions and enable advance preparation for potential threats. The article has been published in Natural Hazards. The research was carried out with support from the Russian Science Foundation (link in Russian).
Centre for Language and Brain Begins Cooperation with Academy of Sciences of Sakha Republic
HSE University's Centre for Language and Brain and the Academy of Sciences of the Republic of Sakha (Yakutia) have signed a partnership agreement, opening up new opportunities for research on the region's understudied languages and bilingualism. Thanks to modern methods, such as eye tracking and neuroimaging, scientists will be able to answer questions about how bilingualism works at the brain level.
How the Brain Responds to Prices: Scientists Discover Neural Marker for Price Perception
Russian scientists have discovered how the brain makes purchasing decisions. Using electroencephalography (EEG) and magnetoencephalography (MEG), researchers found that the brain responds almost instantly when a product's price deviates from expectations. This response engages brain regions involved in evaluating rewards and learning from past decisions. Thus, perceiving a product's value is not merely a conscious choice but also a function of automatic cognitive mechanisms. The results have been published in Frontiers in Human Neuroscience.
AI Predicts Behaviour of Quantum Systems
Scientists from HSE University, in collaboration with researchers from the University of Southern California, have developed an algorithm that rapidly and accurately predicts the behaviour of quantum systems, from quantum computers to solar panels. This methodology enabled the simulation of processes in the MoS₂ semiconductor and revealed that the movement of charged particles is influenced not only by the number of defects but also by their location. These defects can either slow down or accelerate charge transport, leading to effects that were previously difficult to account for with standard methods. The study has been published in Proceedings of the National Academy of Sciences (PNAS).
Electrical Brain Stimulation Helps Memorise New Words
A team of researchers at HSE University, in collaboration with scientists from Russian and foreign universities, has investigated the impact of electrical brain stimulation on learning new words. The experiment shows that direct current stimulation of language centres—Broca's and Wernicke's areas—can improve and speed up the memorisation of new words. The findings have been published in Neurobiology of Learning and Memory.
‘Services Must Be Flexible’: How Governments Can Use Artificial Intelligence
The HSE International Laboratory for Digital Transformation in Public Administration held a roundtable titled ‘Artificial Intelligence in Public Administration: Current Trends.’ Scholars from Israel, China, and Russia discussed which public services AI can enhance and what key factors must be considered when adopting new technologies.
Artificial Intelligence Improves Risk Prediction of Complex Diseases
Neural network models developed at the HSE AI Research Centre have significantly improved the prediction of risks for obesity, type 1 diabetes, psoriasis, and other complex diseases. A joint study with Genotek Ltd showed that deep learning algorithms outperform traditional methods, particularly in cases involving complex gene interactions (epistasis). The findings have been published in Frontiers in Medicine.