D
Dušan Húsek
Researcher at Academy of Sciences of the Czech Republic
Publications - 74
Citations - 604
Dušan Húsek is an academic researcher from Academy of Sciences of the Czech Republic. The author has contributed to research in topics: Artificial neural network & Cluster analysis. The author has an hindex of 12, co-authored 74 publications receiving 561 citations. Previous affiliations of Dušan Húsek include University of Botswana & Technical University of Ostrava.
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Journal ArticleDOI
Boolean Factor Analysis by Attractor Neural Network
TL;DR: This paper describes neural network implementation of the Boolean factor analysis method with Hebbian learning and a Hopfield-like neural network and shows the efficiency of the method on artificial data containing a known list of factors.
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Informational capacity and recall quality in sparsely encoded Hopfield-like neural network: analytical approaches and computer simulation
TL;DR: A sparsely encoded Hopfield-like attractor neural network is investigated analytically and by computer simulation and it is shown that informational capacity monotonically increases when sparseness increases, while recall quality changes nonmonotonically: initially it decreases and then increases.
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Comparison of four classification methods for brain-computer interface
TL;DR: This paper examines the performance of four classiflers for Brain Com- puter Interface (BCI) systems based on multichannel EEG recordings and shows that the increase in the classifying quality is always accompanied by a signiflcant increase of computational complexity.
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Principles of motor recovery in post-stroke patients using hand exoskeleton controlled by the brain-computer interface based on motor imagery
Alexander A. Frolov,Dušan Húsek,Elena V. Biryukova,P. D. Bobrov,O. A. Mokienko,Alexey Alexandrov +5 more
TL;DR: The description includes the principles and physiological prerequisites of BCI based on motor imagery, biologically adequate principles of exoskeleton design and control and the results of clinical application.
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Recurrent-Neural-Network-Based Boolean Factor Analysis and Its Application to Word Clustering
TL;DR: The results of Boolean factor analysis and fuzzy clustering are shown to be not contradictory, but complementary, and the method is applied to two types of textual data on neural networks in two different languages.