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Evolutionary Weighted Ensemble for EEG Signal Recognition

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F14%3A86089795" target="_blank" >RIV/61989100:27740/14:86089795 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-319-07773-4_20" target="_blank" >http://dx.doi.org/10.1007/978-3-319-07773-4_20</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-07773-4_20" target="_blank" >10.1007/978-3-319-07773-4_20</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Evolutionary Weighted Ensemble for EEG Signal Recognition

  • Original language description

    Recognition of EEG signal is very complex but very important problem. In this paper we focus on simplified classification problem which consists of detection finger movement based on analysis of seven EEG sensors. Signal gathered by each sensor are subsequently classified by respective classification algorithm which is based on data compression and so called LZ-Complexity. To improve the overall accuracy of the system, Evolutionary Weighted Ensemble (EWE) system is proposed. Parameters of the EWE are set in learning procedure which uses tailored for that purpose evolutionary algorithm. To take full advantage of information returned sensor classifiers, setting negative weights are permitted, which significantly elevates overall accuracy. Evaluation of EWE and its comparison against selected classical ensemble algorithm are carried on empirical data consisting of almost 5 hundred samples. The results shows that EWE algorithm exploits knowledge represented by sensor classifiers very effec

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2014

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Advances in Intelligent Systems and Computing. Volume 298

  • ISBN

    978-3-319-07772-7

  • ISSN

    2194-5357

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    201-210

  • Publisher name

    Springer

  • Place of publication

    Heidelberg

  • Event location

    Shenzhen

  • Event date

    Jun 13, 2014

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article