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Classification Algorithms Comparison for EEG Movement Recognition

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A00145377" target="_blank" >RIV/68407700:21230/08:00145377 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Classification Algorithms Comparison for EEG Movement Recognition

  • Original language description

    This article provides a comparison of algorithms for single-trial EEG classification. EEG accompanying fast extension (E) and flexion (F) movement of right index finger is classified. The aim of our study was to verify the necessity of using the EEG timecontext information for classification of very closely localized and similar movements. Comparison of our system based on Hidden Markov Models (HMM) with Support Vector Machine (SVM) and Perceptron is presented. The application field of our research results is in the BCI domain to improve the movement classification resolution of the existing systems.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JA - Electronics and optoelectronics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2008

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Analysis of Biomedical Signals and Images; Biosignal 2008 proceedings

  • ISBN

    978-80-214-3613-8

  • ISSN

    1211-412X

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

  • Publisher name

    VUTIUM Press

  • Place of publication

    Brno

  • Event location

    Brno

  • Event date

    Jun 29, 2008

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article