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Classification Control Process
Uher, Daniel ; Musil, Martin (referee) ; Pernikář, Jiří (advisor)
This work deals with design of monitoring systém quantification methodology, used for evaluation of car steering systems quality characteristics. Designed methodology has been prepared directly for company TRW Dačice, but the general techniques of this work could be applied in processes of any test laboratory.
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Reference signals in intracranial EEG: implementation and analysis
Uher, Daniel ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
The idea of a artifact-free brain activity recording has been circling around the scientific world for a few decades. Parasitic phenomenons and unwanted components may significatntly complicate the analysis of intracranial electroencephalographic (iEEG) recordings. However, with the rise of modern technology, new methods for precise removal of noise artifacts started to emerge. Here we use the concept of virtual reference signals for the elimination of such unwanted components. In this work, the algorithms for reference signal estimation using common average based method as well as more recent methods based on independent component analysis (ICA) were realized and evaluated on a variety of iEEG data. It was found that the ICA-based algorithms allow obtaining more accurate estimation of the reference signal as compared to the average-based one. Finally, all the methods were implemented into a open-source Python package đť‘źđť‘’đť‘“đť‘ đť‘–đť‘”, which is publicly available, easy to install and ready to use.
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Reference Signals In Intracranial Eeg:
Implementation And Analysis
Uher, Daniel
The idea of an artifact-free brain activity recording has been circling around the scientific world for a few decades. Noise present in brain activity recordings may complicate the process of evaluation and interpretation. For the elimination of such unwanted components, the concept of virtual reference signals is usually used. In this work, the algorithms for reference signal estimation using common average-based method as well as more recent methods based on independent component analysis (ICA) were realized and evaluated on a new set of real clinical data. It was found that the ICA-based algorithms allow obtaining more accurate estimation of the reference signal as compared to the average-based one. Finally, all the methods were implemented into a free installable Python toolbox, which will be publicly available after additional testing on real data.
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Reference signals in intracranial EEG: implementation and analysis
Uher, Daniel ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
The idea of a artifact-free brain activity recording has been circling around the scientific world for a few decades. Parasitic phenomenons and unwanted components may significatntly complicate the analysis of intracranial electroencephalographic (iEEG) recordings. However, with the rise of modern technology, new methods for precise removal of noise artifacts started to emerge. Here we use the concept of virtual reference signals for the elimination of such unwanted components. In this work, the algorithms for reference signal estimation using common average based method as well as more recent methods based on independent component analysis (ICA) were realized and evaluated on a variety of iEEG data. It was found that the ICA-based algorithms allow obtaining more accurate estimation of the reference signal as compared to the average-based one. Finally, all the methods were implemented into a open-source Python package đť‘źđť‘’đť‘“đť‘ đť‘–đť‘”, which is publicly available, easy to install and ready to use.
|
|
| |
|
|
Classification Control Process
Uher, Daniel ; Musil, Martin (referee) ; Pernikář, Jiří (advisor)
This work deals with design of monitoring systém quantification methodology, used for evaluation of car steering systems quality characteristics. Designed methodology has been prepared directly for company TRW Dačice, but the general techniques of this work could be applied in processes of any test laboratory.
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