Deep Learning Techniques for Classification of P300 Component
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43951149" target="_blank" >RIV/49777513:23520/18:43951149 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Deep Learning Techniques for Classification of P300 Component
Original language description
Deep learning techniques have proved to be beneficial in many scientific disciplines and have beaten state-of-the-art approaches in many applications. The main aim of this article is to improve the success rate of deep learning algorithms, especially stacked autoencoders, when they are used for detection and classification of P300 event-related potential component that reflects brain processes related to stimulus evaluation or categorization. Moreover, the classification results provided by stacked autoencoders are compared with the classification results given by other classification models and classification results provided by combinations of various types of neural network layers
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
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
BIOSTEC 2018 Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies Volume 5: HEALTHINF
ISBN
978-989-758-281-3
ISSN
—
e-ISSN
neuvedeno
Number of pages
8
Pages from-to
446-453
Publisher name
SCITEPRESS – Science and Technology Publications
Place of publication
Setúbal
Event location
Madeira, Portugal
Event date
Jan 19, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—