Self-organizing Maps for Event-Related Potential Data Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F14%3A43922108" target="_blank" >RIV/49777513:23520/14:43922108 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Self-organizing Maps for Event-Related Potential Data Analysis
Original language description
Event-related potentials (ERPs) and especially the P300 component have been gaining attention in braincomputer interface design and neurobiological research. The detection of the P300 component in electroencephalographic signal is challenging since its signal-to-noise ratio is very low. Instead of using traditional supervised pattern recognition, this paper discusses using unsupervised neural networks for the P300 classification purposes. To validate the proposed approach, a method for the P300 detection based on matching pursuit and self-organizing maps is proposed and evaluated. The results may be applied to the design of brain-computer interfaces.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Healthinf 2014 - Proceedings of the international conference on health informatics
ISBN
978-989-758-010-9
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
387-392
Publisher name
SciTePress
Place of publication
Setúbal
Event location
Angers, Francie
Event date
Mar 3, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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