Wyniki wyszukiwania - BazTech

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Validation of Emotiv EPOC+ for extracting ERP correlates of emotional face processing

Kotowski K., Stapor K., Leski J., Kotas M.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

773--781

EN

The article presents our proposed adaptation of the commercially available Emotiv EPOC+ EEG headset for neuroscience research based on event-related brain potentials (ERP). It solves Emotiv EPOC+ synchronization problems (common to most low-cost systems) by applying our proposed stimuli marking circuit. The second goal was to check the capabilities of our modification in neuroscience experiments on emotional face processing. Results of our experiment show the possibility of measuring small differences in the early posterior negativity (EPN) component between neutral and emotional (angry/happy) stimuli consistently with previous works using research-grade EEG systems.

2

Improving the efficacy of automated fetal state assessment with fuzzy analysis of delivery outcome

Czabanski R., Jezewski M., Horoba K., Jezewski J., Leski J.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

223--230

EN

A number of methods of the qualitative assessment of fetal heart rate (FHR) signals are based on supervised learning. The classification methods based on the supervised learning require a set of training recordings accompanied by the reference interpretation. In the real data collections the class of signals related to fetal distress is usually under-represented. Too small percentage of distress patterns adversely affects the effectiveness of the automated evaluation of the fetal state. The paper presents a method of equalizing the class sizes based on the reference assessment of the fetal state with the fuzzy analysis of the newborn attributes. The supervised learning with increased number of the FHR signals, which are characterized by the highest rate of the fuzzy inference leads to significant increase of the efficacy of the qualitative assessment of the fetal state using the Lagrangian support vector machine.

3

A new approach for the clustering using pairs of prototypes

Jezewski M., Czabanski R., Leski J., Horoba K.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

113--121

EN

In the presented work two variants of the fuzzy clustering approach dedicated for determining the antecedents of the rules of the fuzzy rule-based classifier were presented. The main idea consists in adding additional prototypes (’prototypes in between’) to the ones previously obtained using the fuzzy c-means method (ordinary prototypes). The ’prototypes in between’ are determined using pairs of the ordinary prototypes, and the algorithm based on distances and densities finding such pairs was proposed. The classification accuracy obtained applying the presented clustering approaches was verified using six benchmark datasets and compared with two reference methods.