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Automatic Classification of EEG graphoelements (workshop)

The result's identifiers

  • Result code in 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>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Automatic Classification of EEG graphoelements (workshop)

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    O - Miscellaneous

  • CEP classification

    JC - Computer hardware and software

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2014

  • Confidentiality

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