Zuzana Rošťáková
International projects
ReHaB – Towards an ecologically valid symbiosis of BCI and head-mounted VR displays: focus on collaborative post-stroke neurorehabilitation | |
Smerovanie k spoľahlivej a uživateľsky prijateľnej symbióze BCI a VR: zameranie na kolaboratívnu neurorehabilitáciu po cievnej mozgovej príhode | |
Program: | ERANET |
Duration: | 1.1.2022 – 31.12.2024 |
Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
Annotation: | A growing body of evidence suggests that integrated technologies of brain-computer interfaces (BCI) and virtual reality (VR) environments provide a flexible platform for a series of neurorehabilitation therapies, including significant post-stroke motor recovery and cognitive-behavioral therapy. When immersed in such an environment, the subject\’s perceptual level of social interaction is often impaired due to the sub-optimal quality of the interface lacking the social aspect of human interactions.The project proposes a user-friendly wearable low-power smart BCI system with an ecologically valid VR environment in which both the patient and therapist collaboratively interact via their person-specific avatar representations. On the one hand, the patient voluntarily, and in a self-paced manner, manages their activity in the environment and interacts with the therapist via a BCI-driven mental imagery process. This process is computed and rendered in real-time on an energy-efficient wearable device. On the other hand, the therapist\’s unlimited motor and communication skills allow him to fully control the environment. Thus, the VR environment may be flexibly modified by the therapist allowing for different occupational therapy scenarios to be created and selected following the patient\’s recovery needs, mental states, and instantaneous responses. |
National projects
EDABSS – EEG data analysis by blind source separation methods | |
Analýza EEG signálu pomocou metód hľadania skrytých zdrojov | |
Program: | Plán obnovy EÚ |
Duration: | 1.9.2024 – 31.8.2026 |
Project leader: | Mgr. Rošťáková Zuzana, PhD. |
Annotation: | Blind source separation (BSS) approaches are unsupervised machine learning methods focused on the detection of hidden, directly unobservable (latent) structure of real-world data. They play a crucial role in image processing, medical imaging, and music. The proposed project focuses mainly on human electroencephalogram (EEG), for which BSS is beneficial when detecting the narrowband brain oscillations representing brain processes either in health or disease. Two-dimensional BSS methods like principal or independent component analysis are easily applicable and understandable for a broader medical and neurophysiological community. However, the estimated latent component properties are usually incompatible with the real electrophysiological signal character. Consequently, they miss their neurophysiological interpretation. Tensor decomposition is a complex but more flexible mathematical procedure that allows adapting the model structure and constraints to the solution to mimic real-world signal characteristics. The proposed project focuses on tensor decomposition as a tool for i) EEG preprocessing, artefact detection and removal, ii) EEG latent structure analysis using a nonnegative tensor decomposition with block structure allowing to model various relationships between latent components, and iii) post-decomposition analysis of latent component dynamic properties. Obtaining comprehensive information about EEG latent structure and developing novel, user-friendly algorithms is crucial for better understanding brain processes and new methods for treating neurophysiological diseases and disorders. |
Smart deep brain stimulation as a treatment strategy in treatment-resistant depression | |
Inteligentná hĺbková mozgová stimulácia ako inovatívna stratégia pre liečbu mozgových porúch | |
Program: | VEGA |
Duration: | 1.1.2022 – 31.12.2025 |
Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
Annotation: | Impaired connectivity between different brain areas underlines the pathophysiology of multiple brain disorders. It is possible that impaired connectivity between the prefrontal cortex and ventral pallidum is involved in depression. Smart deep brain simulation, combining real-time detection of the neuronal activity in the prefrontal cortex with the stimulation of the ventral tegmental area might be thus effective in depression. We aim to examine the cortico-tegmental connectivity and to test the antidepressant-like effectiveness of the smart deep brain stimulation in an animal model of depression. |
Causal analysis of measured signals and time series | |
Kauzálna analýza nameraných signálov a časových radov | |
Program: | VEGA |
Duration: | 1.1.2022 – 31.12.2025 |
Project leader: | RNDr. Krakovská Anna, CSc. |
Annotation: | The project is focused on the causal analysis of measured time series and signals. It builds on the previous results of the team, concerning the generalization of the Granger test and the design of new tests in the reconstructed state spaces. The aim of the project is the development of new methods for bivariate and multidimensional causal analysis. We will see the investigated time series and signals as one-dimensional manifestations of complex systems or subsystems. We will also extend the detection of causality to multivariate cases – dynamic networks with nodes characterized by time series. Such complex networks are common in the real world. Biomedical applications are among the best known. Brain activity, determined by multichannel electroencephalographic signals, is a crucial example. We want to help show that causality research is currently at a stage that allows for ambitious goals in the study of effective connectivity (i.e., directed interactions, not structural or functional links) in the brain. |