Application of Stacked Autoencoders to P300 Experimental Data

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of Stacked Autoencoders to P300 Experimental Data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Application of Stacked Autoencoders to P300 Experimental Data

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Artificial Intelligence and Soft Computing

ISBN

978-3-319-59062-2

ISSN

0302-9743

e-ISSN

neuvedeno

Počet stran výsledku

12

Strana od-do

187-198

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Zakopané

Datum konání akce

11. 6. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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