Roman Rosipal UM-SAS SAS

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Research interests
  • Research in the area of applied statistics, machine learning, computational and cognitive neuroscience
  • Multivariate data analysis; latent variable regression, classification and dimensionality reduction methods;
    Dynamic Bayesian Networks for data fusion; nonlinear kernel learning and support vector machines
  • Electrophysiological data analysis (EEG,EOG, EMG, ECG); event-related potentials; sleep process modelling;
    study of cognitive fatigue; brain-computer interfaces; vigilance, drowsiness and fatigue monitoring
Current Research projects
  • Enhancing cognition and motor rehabilitation using mixed reality (ECoReMiR) (2017-2021).
    Technological advancements based on mixed reality (MR) offer various challenges for research and medical treatment. The project focuses on two objectives related to healthy subjects and hemiparetic patients after stroke. First, we will test the hypothesis whether cognitive training using appropriately designed MR environment will enhance perceptual and cognitive performance in healthy subjects. This will be tested by computerized psychological experiments as well as by measuring event-related potentials or ERPs. Second, we will test the hypothesis whether experience with training in MR (in combination with motor-imagery based brain-computer interface developed by us) will enhance oscillatory sensory-motor rhythms. This will be tested by measuring subject's EEG activity before and after each training session, clinical testing, as well as by the questionnaires aiming to learn about human factors including mental fatigue, motivation, irritation or sleepiness due to training. In both objectives, we will design and implement a set of testing procedures, carry out a battery of dedicated experiments, and critically evaluate the results with the goal to validate MR designs.
Recent Research projects
  • Effects of sleep disturbances on day-time neurocognitive performance in patients with stroke (SleepCog) (2013-2016).
    Sleep deprivation, whether from disorder or lifestyle, whether acute or chronic, poses a significant risk in day-time cognitive performance, excessive somnolence, impaired attention or decreased level of motor abilities. Sleep deprivation is closely related to sleep fragmentation often associated with short several second long arousals. Although limited studies of partial sleep restriction and sleep fragmentation have revealed important sleep indices leading to cognitive deficits, a challenging question how a typical, good quality, structure of sleep should look like remains open. To improve these results the project will investigate and evaluate a novel probabilistic sleep model. In a preliminary series of tests on healthy subjects it has been shown that the model contains significantly more objective information about external measures of the sleep quality than the traditional sleep staging. Patients with specific cerebral lesions will be studied in the project. It is well-known that targeted patients are strongly vulnerable to sleep disturbances that often lead to deficits in their day-time cognitive and attentional performance. Experimental studies with such patients and with focus on relating their sleep patterns and disturbances with a day-time performance are limited and so far have not been carried out in Slovakia. It is expected that the project will deliver not only new academic research results but also important clinical knowledge.
  • Brain-computer interface with robot-assisted training for rehabilitation (BCI-RAS) (2013-2017).
    We will apply advanced tools and methods of applied informatics for the design and development of an intelligent system allowing the users to go through the process of self-controlled training of impaired motor pathways. We will combine the brain-computer interface (BCI) technology with a robotic arm system into a compact system that can be used as a robot-assisted neurorehabilitation tool. The BCI directly uses the signal of the brain electrical activity to allow users to operate the environment without any muscular activation. However, several critical issues need to be addressed before using BCI in neurorehabilitation, namely, issues ranging from signal acquisition and selection of the proper BCI paradigm to the evaluation of the affective state, cognitive load and system acceptability of the users. The project will address these issues by using new signal processing and machine learning algorithms, training protocols and intelligent methods for the users' physiological state changes detection and monitoring, recently developed by the researchers participating in the project. Novel BCI training protocols, including the neurofeedback training based on multi-way analysis of EEG data, will be used and validated. Finally, the system will be tested in clinical practice on selected patients with motor impairments caused by stroke, as well as on healthy volunteers.
  • The Neurosensory Optimization of Information Transfer (NOIT) (2011-2014).
    The NOIT project aims to show that we can counteract the effects of sleep deprivation and fatigue on cognition with the aid of an automated EEG biofeedback (EBF) system that enables us to continually manage processes in our left and right brain hemispheres.
Research developments
  • APECS stands for Advanced Physiological Estimation of Cognitive Status, a system of signal processing and machine learning algorithms for mental state estimation. APECSgui is a MATLAB toolbox that allows non-experts to build EEG-based models for mental state estimation.
  • EEtrac is a computer system for simultaneous real-time processing of EEG and Eye-tracking signals. It allows a laptop computer to display information which is dually contingent on the mental state and gaze of the user.
  • PSM stands for Probabilistic Sleep Model, a model of the sleep process with a higher temporal and spatial resolution based on information fused from a set of different sensors, The model is based on so-called Gaussian mixture models that cluster spectral characteristics of the signals into a number of sleep states with a high temporal resolution and without a prior definition of how many and which states are reached during a night of sleep. Thus, the model frees itself from some of the limits of classical sleep signal analysis, namely that stages are defined by what an expert can identify visually in the signal and by the arbitrary rough division into 30 second pieces, historically still rooted in the use of paper EEG. The proof that this new way of describing sleep correlates better with how a patient feels and performs in the morning points to the clinical validity of the approach which could lead to new ways of analyzing sleep in medicine and beyond.