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Typing Using Brain Signals
Wagner, Lukáš ; Malinka, Kamil (referee) ; Tinka, Jan (advisor)
This bachelor thesis focusses on the implementation of a brain-computer interface, programmed in Python language, that would enable to communicate using EEG. The thesis investigates and evaluates existing brain-computer interface technologies for this purpose. The thesis also explores the use of machine learning applied to the technology, in particular neural networks, which have proven to be one of the most accurate methods of EEG signal processing. Following that, 3 different systems are proposed and implemented, each on different paradigm of visually evoking EEG potential changes. These systems were tested with different signal classification approaches. Unfortunately, none of the systems proved to be useful in communication.
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Typing Using Brain Signals
Wagner, Lukáš ; Malinka, Kamil (referee) ; Tinka, Jan (advisor)
This bachelor thesis focusses on the implementation of a brain-computer interface, programmed in Python language, that would enable to communicate using EEG. The thesis investigates and evaluates existing brain-computer interface technologies for this purpose. The thesis also explores the use of machine learning applied to the technology, in particular neural networks, which have proven to be one of the most accurate methods of EEG signal processing. Following that, 3 different systems are proposed and implemented, each on different paradigm of visually evoking EEG potential changes. These systems were tested with different signal classification approaches. Unfortunately, none of the systems proved to be useful in communication.
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Limitations of variant consequence predictors
Břicháčková, Kateřina ; Daněček, Petr (advisor) ; Kolář, Michal (referee)
Thanks to numerous large-scale sequencing projects, the number of discovered genomic variants is increasing. The key step in analyzing the variant data is the functional annotation, since it helps researchers and clinicians to categorize, filter and prioritize the variants for further research. This thesis discusses five commonly-used variant consequence predictors, offers advice on how to use them and briefly goes through the algorithms they employ. Moreover, various data formats as well as the human reference genome and different genome annotations are described in the thesis. The correctness of the reference is of great importance as all the predictors rely on it. This thesis highlights some situations in which the results given by different predictors can vary. All the tests were made using the Ensembl gene annotation (release 92) and the GRCh38 reference assembly.
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