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
—