Aleš Belič

Publications (5)1.65 Total impact

• Source

  Available from: Vito Logar

  Article: Brain-computer interface analysis of a dynamic visuo-motor task.

  Vito Logar, Aleš Belič
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  ABSTRACT: The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain-computer interface. For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filtering, double phase demodulation and double principal component analyses (PCA), each with a separate set of parameters. For the movement-prediction model a fuzzy inference system was used. The results have shown that the EEG signals measured during the dVM tasks carry enough information about the subjects' wrist movements for them to be successfully decoded using the presented methodology. Reasonably high values of the correlation coefficients suggest that the validation of the proposed approach is satisfactory. Moreover, since the causality of the rhythm filtering and the PCA transformation has been achieved, we have shown that these methods can also be used in a real-time, brain-computer interface. The study revealed that using non-causal, optimized methods yields better prediction results in comparison with the causal, non-optimized methodology; however, taking into account that the causality of these methods allows real-time processing, the minor decrease in prediction quality is acceptable. The study suggests that the methodology that was proposed in our previous studies is also valid for identifying the EEG-coded content during dVM tasks, albeit with various modifications, which allow better prediction results and real-time data processing. The results have shown that wrist movements can be predicted in simulated or real time; however, the results of the non-causal, optimized methodology (simulated) are slightly better. Nevertheless, the study has revealed that these methods should be suitable for use in the development of a non-invasive, brain-computer interface.
  Artificial intelligence in medicine 01/2011; 51(1):43-51. · 1.65 Impact Factor
• Chapter: Local Model Networks for the Optimization of a Tablet Production Process

  Benjamin Hartmann, Oliver Nelles, Aleš Belič, Damjana Zupančič–Božič
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  ABSTRACT: The calibration of a tablet press machine requires comprehensive experiments and is therefore expensive and time-consuming. In order to optimize the process parameters of a tablet press machine on the basis of measured data this paper presents a new approach that works with the application of local model networks. Goal of the model-based optimization was the improvement of the quality of produced tablets, i.e. the reduction of capping occurence and the variation of the tablet mass as well as the variation of the crushing strength. Modeling and optimization of the tablet process parameters show that it is possible to find process settings for the tabletting of non-preprocessed powder such that a sufficient quality of the tablets can be achieved.
  01/1970: pages 241-250;
• Source

  Available from: Vito Logar

  Article: GRIPPING FORCE PREDICTION USING FUZZY MODEL AND EEG SIGNALS

  Vito Logar, Igoř Skrjanc, Aleš Belič, Rihard Karba, Blaž Koritnik, Simon Brežan, Veronika Rutar, Janez Zidar
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  ABSTRACT: The exact mechanism of informational integration between different brain regions is still not known. The theory of binding tries to explain how different aspects of perception and action functionally integrate in the brain, but it is still not known, how the transferred information is coded. The present paper reports of fuzzy identification of brain-code during simple gripping force control tasks using Takagi-Sugeno fuzzy inference system. The study tries to show that there is enough information in the EEG signals that would allow gripping force prediction and thus provides some new insights into the brain functioning as well as different approaches to man-machine interface. This study suggests that it should be possible to achieve continuous control of machines using EEG signals. The prediction quality of the presented system is not of a very high quality, however, that is not necessary, since many studies show that relatively small amount of training is necessary to control EEG patterns. The presented interface would only reduce the amount of training for the person, as it is capable of interpreting the natural brain-code.
• Source

  Available from: uni-lj.si

  Article: Minimisation of the capping tendency by tableting process optimisation with the application of artificial neural networks and fuzzy models

  Aleš Belič, Igor Škrjanc, Damjana Zupančič Božič, Rihard Karba, Franc Vrečer
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  ABSTRACT: The pharmaceutical industry is increasingly aware of the advantages of implementing a quality-by-design (QbD) principle, including process analytical technology, in drug development and manufacturing. Although the implementation of QbD into product development and manufacturing inevitably requires larger resources, both human and financial, large-scale production can be established in a more cost-effective manner and with improved efficiency and product quality. The objective of the present work was to study the influence of particle size (and indirectly, the influence of dry granulation process) and the settings of the tableting parameters on the tablet capping tendency. Artificial neural network and fuzzy models were used for modelling the effect of the particle size and the tableting machine settings on the capping coefficient. The suitability of routinely measured quantities for the prediction of tablet quality was tested. Results showed that model-based expert systems based on the contemporary routinely measured quantities can significantly improve the trial-and-error procedures; however, they cannot completely replace them. The modelling results also suggest that in cases where it is not possible to obtain sufficient number of measurements to uniquely identify the model, it is beneficial to use several modelling techniques to identify the quality of model prediction.
  European Journal of Pharmaceutics and Biopharmaceutics.
• Article: Tableting process optimisation with the application of fuzzy models

  Aleš Belič, Igor Škrjanc, Damjana Zupančič Božič, Franc Vrečer
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  ABSTRACT: A quality-by-design (QbD) principle, including process analytical technology, is becoming the principal idea in drug development and manufacturing. The implementation of QbD into product development and manufacturing requires larger resources, both human and financial, however, large-scale production can be established in a more cost-effective manner and with improved product quality. The objective of the present work was to study the influence of particle size distribution in powder mixture for tableting, and the settings of the compression parameters on the tablet quality described by the capping coefficient, standard deviations of mass and crushing strength of compressed tablets. Fuzzy models were used for modelling of the effects of the particle size distribution and the tableting machine settings on the tablet quality. The results showed that the application of mathematical models, based on the contemporary routinely measured quantities, can significantly improve the trial-and-error procedures.
  International Journal of Pharmaceutics.