Projects

Jozef Jakubík

Project selection:

National projects

CARE-BCI – Cooperative AI-enhanced BCI-HMD rehabilitation for post-stroke recovery
Kooperatívna AI BCI-HMD rehabilitácia pre pacientov po cievnej mozgovej príhode
Program: SRDA
Duration: 1.9.2026 – 31.8.2030
Project leader: Ing. Mgr. Rosipal Roman, DrSc.
Annotation: This project aims to advance post-stroke neurorehabilitation through the development of an artificial intelligence (AI)-enhanced, collaborative brain–computer interface (BCI) system integrated with immersive head-mounted display (HMD)–based virtual reality (VR). AI serves as a central enabling component, supporting adaptive neural decoding, cognitive-state monitoring, and data-driven optimization of rehabilitation protocols. A key focus is on the creation ofcooperative, shared-action rehabilitation environments, in which the patient and therapist jointly perform the same task in real time. This combination of AI-driven adaptation and shared-action cooperation moves beyond isolated task execution toward socially interactive, coordinated motor rehabilitation with high ecological validity.The approach extends the state of the art by employing AI for adaptive neural decoding, cognitive-state monitoring, and longitudinal meta-analysis of rehabilitation trajectories. Active BCI components use personalized models to decode motor imagery under inter- and intra-subject variability, while passive BCI continuously monitors cognitive workload, mentalfatigue, and engagement. An exploratory component investigates the feasibility of an AI-assisted therapeutic agent capable of partially supporting therapist actions within immersive, cooperative VR environments, while preserving safety, interpretability, and clinical oversight.The ambition is to establish a scalable and personalized neurorehabilitation framework that enhances therapeutic efficacy, strengthens patient–therapist interaction through shared-action VR tasks, and reduces therapist workload. By integrating active and passive BCI, cooperative VR, and explainable AI within a single coherent system, the project aims to generatenew scientific insights into rehabilitation dynamics and provide a clinically relevant pathway toward accessible, data-driven post-stroke rehabilitation in clinical and home-based settings.
CAUSMET – Methods and algorithms for causal analysis and quantification of measurement uncertainty
Názov projektu Metódy a algoritmy kauzálnej analýzy a kvantifikácie neistôt meraní
Program: SRDA
Duration: 1.9.2026 – 31.12.2029
Project leader: Doc. RNDr. Witkovský Viktor, CSc.
Annotation: The project develops advanced methods and algorithms for causal analysis of stochastic and deterministic processes and for quantifying measurement uncertainties. It addresses methodological challenges in the analysis of time series and dynamical data, where correlation alone is insufficient to reveal the mechanisms governing system behavior. Manyapplications, therefore, require identifying causal relations between variables while reliably characterizing uncertainties arising from measurement processes, noise, and incomplete observations.The project will develop classical and modern approaches to causal analysis of time series based on probabilistic and statistical modeling, and integrate them with algorithms enabling statistical inference and prediction in the presence of randomness, measurement errors, and uncertainty.Modern applications in physical, biomedical, economic, environmental, and linguistic measurements, as well as in the social sciences (education, psychology), generate large and complex datasets with intricate dependence structures and temporal dynamics. A significant project component is hence the study of stochastic dynamical models, including diffusion processes, as a natural framework for modeling random dynamics observed via measurement time series. When modeling complex temporal or spatio-temporal data using kriging, causal structure will serve as a key starting point.The project also advances uncertainty methods for quantifying measurement uncertainties in line with modern metrology and aims to establish a unified methodological framework combining causal analysis, dynamical modeling, and statistical inference and forecasting. Interdisciplinary collaboration among the Institute of Measurement Science of the SAS, theMathematical Institute of the SAS, and the Faculty of Science of P. J. Šafárik University creates favorable conditions for the development of new theoretical results, efficient algorithms, and their applications.