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Synchronization of measuring devices by optical fibres
Petr, Ondřej ; Šebesta, Jiří (referee) ; Dřínovský, Jiří (advisor)
The aim of this work was to get acquainted with the possibility of transmission of RF signals using optical cables. And design synchronization modules to transmit the reference signal, so that the source and the receiver of the signal are time-synchronized and mutually galvanically separated using optical cable. To transmit and receive optical signals through optical fiber, I chose the transmitter and receiver of the series HBR- 0400 the company Agilent Technologies.
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Synchronization of measuring devices by optical fibres
Petr, Ondřej ; Šebesta, Jiří (referee) ; Dřínovský, Jiří (advisor)
The aim of this work was to get acquainted with the possibility of transmission of RF signals using optical cables. And design synchronization modules to transmit the reference signal, so that the source and the receiver of the signal are time-synchronized and mutually galvanically separated using optical cable. To transmit and receive optical signals through optical fiber, I chose the optical transmitter and receiver of the series HBR- 0400 the company Agilent Technologies.
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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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Artefacts Removal from Brain EEG Signals Using Adaptive Algorithms
Hatala, Juraj ; Jawed, Soyiba (referee) ; Shakil, Sadia (advisor)
Tato práce se zabývá problémem artefaktů ve záznamech elektroencefalografie (EEG) a metodami jejich odstranění s důrazem na adaptivní filtrace. Artefakty jsou neodmys- litelnou součástí metody EEG a negativně ovlivňují analýzu výsledků tím, že překrývají zájmové mozkové signály. Adaptivní filtrace je všestrannou metodou, kterou lze použít pro odstranění těchto artefaktů, pokud je k dispozici referenční signál korelovaný s arte- faktem. Hlavním cílem této práce je návrh a implementace frameworku, který umožní aplikaci metod adaptivní filtrace na EEG data. Druhotným cílem je posouzení účinnosti nového algoritmu Q-LMS při odstraňování artefaktů z EEG, protože dosud nebyl v tomto scénáři použit. Práce představuje knihovnu v prostředí Python pro adaptivní filtrace EEG a ukazuje a hodnotí experimenty pro scénáře odstraňování artefaktů s použitím Q-LMS fil- tru implementovaného v navržené knihovně. V této knihovně je uživatel schopen vytvářet přizpůsobitelné filtrační pipeliny. Knihovna nabízí různé adaptivní filtry a metody vytváření referenčního signálu s důrazem na zpracování neurologických dat ve formátu BIDS. Uži- vatel však může sdílet vlastní filtry s frameworkem a také používat vlastní vstupní data a referenční signály. Experimenty s Q-LMS algoritmem ukázaly, že se jedná o dobře fun- gující adaptivní algoritmus, avšak výsledky filtrace byly průměrný ve srovnání s výsledky dosaženými jinými standardními adaptivními algoritmy
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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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Synchronization of measuring devices by optical fibres
Petr, Ondřej ; Šebesta, Jiří (referee) ; Dřínovský, Jiří (advisor)
The aim of this work was to get acquainted with the possibility of transmission of RF signals using optical cables. And design synchronization modules to transmit the reference signal, so that the source and the receiver of the signal are time-synchronized and mutually galvanically separated using optical cable. To transmit and receive optical signals through optical fiber, I chose the transmitter and receiver of the series HBR- 0400 the company Agilent Technologies.
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Synchronization of measuring devices by optical fibres
Petr, Ondřej ; Šebesta, Jiří (referee) ; Dřínovský, Jiří (advisor)
The aim of this work was to get acquainted with the possibility of transmission of RF signals using optical cables. And design synchronization modules to transmit the reference signal, so that the source and the receiver of the signal are time-synchronized and mutually galvanically separated using optical cable. To transmit and receive optical signals through optical fiber, I chose the optical transmitter and receiver of the series HBR- 0400 the company Agilent Technologies.
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