Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Prokop, Tomáš | |
| dc.date.accessioned | 2019-11-28T06:12:09Z | |
| dc.date.available | 2019-11-28T06:12:09Z | |
| dc.date.issued | 2017 | |
| dc.identifier.uri | http://www.kiv.zcu.cz/cz/vyzkum/publikace/technicke-zpravy/ | |
| dc.identifier.uri | http://hdl.handle.net/11025/35988 | |
| dc.format | 47 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | Západočeská univerzita v Plzni | cs |
| dc.rights | © Západočeská univerzita v Plzni | cs |
| dc.subject | elektroencefalografie | cs |
| dc.subject | zpracování signálu | cs |
| dc.subject | Hilbert Huangova transformace | cs |
| dc.title | Heterogeneous Medical Data Processing | en |
| dc.type | report | en |
| dc.type | zpráva | cs |
| dc.rights.access | openAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | Electroencephalography (EEG) is a popular technique used for example in diagnostics of diseases, sleep monitoring and neurorehabilitations. Due to increasing mobility and decreasing price of EEG measuring devices EEG and ERP (event related potential) techniques have become more widespread also in assistive technologies and brain-jogging. As the result the amount of EEG data has been increasing and research into EEG signal processing and classification has become again more necessary. This thesis focuses on the state-of-the-art related to the methods of EEG/ERP signal processing and classification, but follows a standard processing workflow starting from signal acquisition and preprocessing to feature extraction and classification. The commonly used time-frequency domain methods (Wavelet transform and Matching pursuit) that are suitable for feature extraction are described together with the Hilbert-Huang Transform (HHT). HHT uses a new approach of multi-channel signal decomposition called the Multivariate Empirical Mode Decomposition. The described classification methods are divided into two groups. Linear classifiers are represented by the Linear Discriminant Analysis and Support Vector Machines. The second group, neural networks, focuses on the Multi-Layer Perceptron and a set of classification algorithms called deep learning neural networks. These are composed of many layers of neurons while the Multi-Layer Perceptron typically contained only two layers because of the backpropagation problem. Some of the deep learning algorithms have been reported to beat state-of-the-art approaches in many applications and that is why further research in the EEG domain seems to be beneficial. | en |
| dc.subject.translated | electroencephalography | en |
| dc.subject.translated | signal processing | en |
| dc.subject.translated | Hilbert-Huang Transform | en |
| Vyskytuje se v kolekcích: | Zprávy / Reports (KIV) | |
Soubory připojené k záznamu:
| Soubor | Popis | Velikost | Formát | |
|---|---|---|---|---|
| Prokop.pdf | Plný text | 2,05 MB | Adobe PDF | Zobrazit/otevřít |
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