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Application of Stacked Autoencoders to P300 Experimental Data

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43931961" target="_blank" >RIV/49777513:23520/17:43931961 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-319-59063-917" target="_blank" >http://dx.doi.org/10.1007/978-3-319-59063-917</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-59063-917" target="_blank" >10.1007/978-3-319-59063-917</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Application of Stacked Autoencoders to P300 Experimental Data

  • Original language description

    Deep learning has emerged as a new branch of machine learning in recent years. Some of the related algorithms have been reported to beat state-of-the-art approaches in many applications. The main aim of this paper is to verify one of the deep learning algorithms, specifically a stacked autoencoder, to detect the P300 component. This component, as a specific brain response, is widely used in the systems based on brain-computer interface. A simple brain-computer interface experiment more than 200 school-age participants was performed to obtain large datasets containing the P300 component. After feature extraction the collected data were split into the training and testing sets. State-of-the art BCI classifiers (such as LDA, SVM, or Bayesian LDA) were applied to the data and then compared with the results of stacked autoencoders.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Artificial Intelligence and Soft Computing

  • ISBN

    978-3-319-59062-2

  • ISSN

    0302-9743

  • e-ISSN

    neuvedeno

  • Number of pages

    12

  • Pages from-to

    187-198

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Zakopané

  • Event date

    Jun 11, 2017

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article