Our most important research publications

Abstract: Attention of drivers is a key factor of road safety. Since inattentive drivers cause a considerable number of accidents, it is worth to examine the causes and course of driver's attention even in laboratory conditions during a simulated drive. This paper deals with the experiment in which the methods of electroencephalography and event related potentials are used under various conditions to investigate driver's attention. Eleven participants, university students, were stimulated with audio signals during monotonous drive in four experimental sessions. The hypothesis is that the peak latency of the P3 component increases in time as the driver is more tired from monotonous drive, daytime and sleep deprivation. The background of the used methods, experimental design, participants, data processing, results and final discussion are presented in this paper.

Abstract: The study deals with investigation and comparison of the auditory attention performance of children with developmental coordination disorder (DCD) and normally developing children (NDC) using cognitive evoked potentials (ERPs) in passive conditions. ERPs data showed that children with DCD have less ability to detect small physical differences between acoustic stimuli (no MMN response in DCD children) and have a reduced attentional engagement and stimulus evaluation of salient stimuli (a reduction of P3 amplitude in DCD children). The results of our study suggest that children with DCD do not only suffer from a visuospatial attention deficit as previous studies reported but also have auditory attention deficit.

Abstract: A diabetic patient occasionally undergoes a detailed monitoring of their glucose levels. Over the course of a few days, a monitoring system provides a detailed track of their interstitial fluid glucose levels measured in their subcutaneous tissue. A discrepancy in the blood and interstitial fluid glucose levels is unimportant because the blood glucose levels are not measured continuously. Approximately five blood glucose level samples are taken per day, and the interstitial fluid glucose level is usually measured every 5 min. An increased frequency of blood glucose level sampling would cause discomfort for the patient; thus, there is a need for methods to estimate blood glucose levels from the glucose levels measured in subcutaneous tissue. The Steil-Rebrin model is widely used to describe the relationship between blood and interstitial fluid glucose dynamics. However, we measured glucose level patterns for which the Steil-Rebrin model does not hold. Therefore, we based our research on a different model that relates present blood and interstitial fluid glucose levels to future interstitial fluid glucose levels. Using this model, we derived an improved model for calculating blood glucose levels. In the experiments conducted, this model outperformed the Steil-Rebrin model while introducing no additional requirements for glucose sample collection. In subcutaneous tissue, 26.71% of the calculated blood glucose levels had absolute values of relative differences from smoothed measured blood glucose levels less than or equal to 5% using the Steil-Rebrin model. However, the same difference interval was encountered in 63.01% of the calculated blood glucose levels using the proposed model. In addition, 79.45% of the levels calculated with the Steil-Rebrin model compared with 95.21% of the levels calculated with the proposed model had 20% difference intervals.

Abstract: This paper presents a methodology for interactive muscle simulation. The fibres of individual muscles are represented by particles connected by springs, thus creating a deformable model of the muscle. In order to be able to describe human musculoskeletal system, contact between pairs of muscles as well as muscles and bones must be accounted for. Therefore, collision detection and response mechanism which allows both types of contact (soft body vs. rigid body and soft vs. soft body) is presented. The solution is a part of a project dedicated to improvement of the effectiveness of osteoporosis prediction and treatment.

Abstract: EEG/ERP (electroencephalography, event-related potential) laboratories produce experimental data and metadata. Authors' research group has contributed to the building of a neuroinformatics infrastructure by developing and integrating data management and analytic tools for EEG/ERP research - the EEG/ERP Portal. With the development of the Portal and the increasing amount of data/metadata, a proper full text search mechanism for efficient information retrieval is necessary to improve the user experience. The presented solution combines search over data/metadata stored in an electrophysiological database and in the LinkedIn social network. Open source search engines, criteria, suitable engine selection, and index design are presented. Integration of the full-text solution to the EEG/ERP Portal is described.

Abstract: The event-related potentials technique is widely used in cognitive neuroscience research. The P300 waveform has been explored in many research articles because of its wide applications, such as lie detection or brain-computer interfaces (BCI). However, very few datasets are publicly available. Therefore, most researchers use only their private datasets for their analysis. This leads to minimally comparable results, particularly in brain-computer research interfaces. Here we present electroencephalography/event-related potentials (EEG/ERP) data. The data were obtained from 20 healthy subjects and was acquired using an odd-ball hardware stimulator. The visual stimulation was based on a three-stimulus paradigm and included target, non-target and distracter stimuli. The data and collected metadata are shared in the EEG/ERP Portal

Abstract: Software tool was needed to verify a model predicting interstitial fluid glucose level, while conducting an experiment. With the tool, several tasks execute concurrently to effectively utilize available processors. Implementing the tool implied addressing such aspects of parallel computing which possibly have a broader impact. In this paper, I present an experience with implementing Lamport-clock ordered event scheme to control a parallel program employing a task-stealing scheduler, while eliminating the possibility of accidentally masking a synchronization error. For a program based on Intel Threading Building Blocks library, I devised a scheme to control task execution with events. These events are ordered using the concept of Lamport Clock. As the causal ordering of events is complete , program?s behavior can be reconstructed for additional debugging. In the implementation devised, recording theevents induces no additional synchronization operations that could accidentally mask a synchronization error. The work is presented in a context of glucose level prediction that originates from a glucose-transporter research.

Abstract: In this paper we present a novel approach of an automatic computation of muscle control curves. It is based on skeletonization of a triangular surface mesh representing the muscle. Automatically determined control curves are then connected to the skeleton of the human body model so as to govern the deformation of the muscle surface when the skeleton moves. The method, which was implemented in C++ using VTK framework, was integrated into the human body framework being developed at our institution and tested on the walking lower limbs. The results show that the control curves produced by the method have a positive effect on the deformation and, therefore, are preferred to manually defined lines of action that are used as control curves in the human body framework at present.

Abstract: Large amounts of EEG/ERP (electroencephalography, event-related potential) data are produced by scientific laboratories. For complex analysis, data are processed by a set of methods sequentially or in parallel. These processes are known as workflows. However, various input/output formats of used methods involve difficulties while putting methods in a pipe. Simple syntactic rules comparing formats of input/output are already used by workflow engines. In electrophysiology, it is necessary to extend these rules due to variety of methods. Therefore, extension of syntactic rules between subsequent methods in a workflow is presented in this paper. The proposed solution allows creating more complex workflows in the domain of electrophysiology.

Abstract: Many efforts are devoted to provide a unified solution for maintaining data from electrophysiological experiments. Because large data collections of heterogeneous nature are obtained, neuroinformatics databases must be robust and flexible. Current database systems are of two types. The first one uses a fixed schema while the second one is schema free. This paper discusses usage of a NoSQL database, MongoDB, for electrophysiological experiments and investigates transformation of existing electroencephalography (EEG) and event-related potentials (ERP) database records in Oracle into MongoDB. Two perspectives, flexibility and performance are discussed. A final approach that profits from combination of both concepts, is also discussed.

Abstract: We adopt an ontology-driven information system design for research purposes. It is adaptable and extensible solution in the situation where the main features are the lack of a stable data model and changing and varying requests. We use ontology-driven design primarily as a collaboration tool for the research. Members directly collaborate and use this information system to ease them tasks with management of source/raw data, software applications and research results for its whole life-time. A different heterogeneous data kinds and a large volume data sets could be stored and shared together with its description meta data between research teams and its members in an uniform manner. It leads to an opportunity to process a stored data using proper methods and tools in a parallel and distributed computing environment. All achieved results are automatically stored and supplemented by an ontology-driven description meta data.

Abstract: This paper describes improvement of the Hilbert-Huang transform (HHT) for detection of ERP components in the EEG signal. Time-frequency domain methods, such as the wavelet transform or matching pursuit, are commonly for this task. We used a modified Hilbert-Huang transform that allows the processing of quasi-stationary signals such as EEG. The essential part of the HHT is an Empirical Mode Decomposition (EMD) that decomposes signal into intrinsic mode functions (IMFs). We designed additional stopping criteria for better selection of IMFs in the EMD. These IMFs positively affect later computed instantaneous attributes and increase classification success. We tested the influence of additional stopping criteria on classification reliability using the real EEG data acquired in our laboratory. Our results demonstrated that we were able to detect the P3 component by using the HHT with additional stopping criteria more successfully than by using the original implementation of modified HHT, continuous wavelet transform and matching pursuit.

Abstract: Relatively recently it has become apparent that providing human kind with a better healthcare requires personalised, predictive and integrative medicine, for which the building of Virtual Physiological Human (VPH) framework accessible via virtual patient avatar is necessary. Real-time modelling and visual exploration of such a complex avatar is a challenging task. In this paper, we propose a real-time method for automatic modelling of an arbitrarily large number of muscle fibres in the volume of a muscle represented by its surface mesh. The method is based on an iterative morphing of predefined fibres template into the muscle volume exploiting harmonic scalar field computed on the surface of muscle. Experiments with muscles of thighs and pelvis show that the method produces realistic shapes of fibres. Our sequential VTK-based C++ implementation is capable of producing 64 fine fibres within a muscle of 10K triangles in less than 170 ms on commodity hardware making the method suitable for VPH purposes as well as for interactive educational medical software.

Abstract: Event-related potentials (ERPs) and especially the P300 component have been gaining attention in braincomputer interface design and neurobiological research. The detection of the P300 component in electroencephalographic signal is challenging since its signal-to-noise ratio is very low. Instead of using traditional supervised pattern recognition, this paper discusses using unsupervised neural networks for the P300 classification purposes. To validate the proposed approach, a method for the P300 detection based on matching pursuit and self-organizing maps is proposed and evaluated. The results may be applied to the design of brain-computer interfaces.

Abstract: As in other areas of experimental science, operation of electrophysiological laboratory, design and performance of electrophysiological experiments, collection, storage and sharing of experimental data and metadata, analysis and interpretation of these data, and publication of results are time consuming activities. If these activities are well organized and supported by a suitable infrastructure, work efficiency of researchers increases significantly. This article deals with the main concepts, design, and development of software and hardware infrastructure for research in electrophysiology. The described infrastructure has been primarily developed for the needs of neuroinformatics laboratory at the University of West Bohemia, the Czech Republic. However, from the beginning it has been also designed and developed to be open and applicable in laboratories that do similar research. After introducing the laboratory and the whole architectural concept the individual parts of the infrastructure are described. The central element of the software infrastructure is a web-based portal that enables community researchers to store, share, download and search data and metadata from electrophysiological experiments. The data model, domain ontology and usage of semantic web languages and technologies are described. Current data publication policy used in the portal is briefly introduced. The registration of the portal within Neuroscience Information Framework is described. Then the methods used for processing of electrophysiological signals are presented. The specific modifications of these methods introduced by laboratory researches are summarized; the methods are organized into a laboratory workflow. Other parts of the software infrastructure include mobile and offline solutions for data/metadata storing and a hardware stimulator communicating with an EEG amplifier and recording software.

Abstract: The Windowed means paradigm is a method for slow-changing cortical potentials feature extraction, most importantly in reaction to events. It has been successfully used for various brain-computer interfaces. The objective of this paper was to evaluate if this paradigm is also appropriate for P300 brain-computer interfaces. The modified method was tested on five healthy subjects. The optimal selection of parameters was discussed. The Windowed means paradigm was successful for the P300 detection on the testing data-set.