Scientific research is focused on the development of theoretical methods in the field of mathematical statistics and applied mathematics, especially the problems of measurement process and evaluation of measured data:
- research of linear and nonlinear regression models with complex covariance structure
- design of models and methods for analyzing measurement results, including methods for determining their uncertainties
- research of statistical and metrological intervals (statistical prediction intervals, confidence intervals, and tolerance intervals, respectively metrological coverage intervals)
- research of calibration models and methods for analyzing results of interlaboratory comparisons
- research on probability distributions determined by combining information from independent sources
- theoretical research of statistical parametric and non-parametric methods for biomedical and technical applications
- research in applied statistics and machine learning methods in EEG processing and analysis
- methods of nonlinear dynamics in signal processing and time series analysis; applications for complex manifestations of biological systems (ECG, EEG) – reconstruction of state portraits, noise reduction, modeling, prediction, classification
- causality detection from measured time series
- investigation of complex systems using characteristics known from the theory of chaos and fractals
- application of machine learning methods and self-organizing neural and other intelligent networks to prediction and classification of time series and signals, especially in biomedicine
- basic and experimental research in the field of cognitive and computational neuroscience
- research in the use of brain-computer interface and virtual reality for neurorehabilitation of the paretic limb
- application of digital image analysis methods in optical microscopy (eg, cell structures) and pattern recognition methods
- development of methods for measurement and analysis of biological effects of electromagnetic fields