Automatic Classification of EEG graphoelements (workshop)

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F14%3A00222920" target="_blank" >RIV/68407700:21460/14:00222920 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Classification of EEG graphoelements (workshop)

Popis výsledku v původním jazyce

1. Motivation, why and which types of EEG graphoelements to classify automatically 2. Discriminative features extraction a. Multichannel adaptive segmentation of non-stationary signals b. Heuristic features extraction based on physician's point of view c. Extraction, selection, reduction and features standardization d. Application of PCA - Principal Component Analysis and ICA- Independent Component Analysis (artefacts rejection) 3. Supervised and non-supervised learning classical and fuzzy. a. Statistical pattern recognition, k-NN, k-means b. Artificial neural networks, multilayer perceptron c. Fuzzy sets for improving the homogeneity classes of EEG segments (fuzzy c-means, fuzzy k-NN) 4. Semi-automatic extraction of prototypes from original EEG recordings, pre-processing by cluster analysis in the learning phase (prototypes gathering), involving of expert into the process of etalons extraction 5. Graphic visualization of results a. Color identification of significant graphoelements b.

Název v anglickém jazyce

Automatic Classification of EEG graphoelements (workshop)

Popis výsledku anglicky

1. Motivation, why and which types of EEG graphoelements to classify automatically 2. Discriminative features extraction a. Multichannel adaptive segmentation of non-stationary signals b. Heuristic features extraction based on physician's point of view c. Extraction, selection, reduction and features standardization d. Application of PCA - Principal Component Analysis and ICA- Independent Component Analysis (artefacts rejection) 3. Supervised and non-supervised learning classical and fuzzy. a. Statistical pattern recognition, k-NN, k-means b. Artificial neural networks, multilayer perceptron c. Fuzzy sets for improving the homogeneity classes of EEG segments (fuzzy c-means, fuzzy k-NN) 4. Semi-automatic extraction of prototypes from original EEG recordings, pre-processing by cluster analysis in the learning phase (prototypes gathering), involving of expert into the process of etalons extraction 5. Graphic visualization of results a. Color identification of significant graphoelements b.

Druh

O - Ostatní výsledky

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů