Correlation-based Neural Gas for Visualizing Correlations between EEG Features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00194840" target="_blank" >RIV/68407700:21230/13:00194840 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.springerlink.com/content/qjh381l0x0455j75/" target="_blank" >http://www.springerlink.com/content/qjh381l0x0455j75/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-33018-6_45" target="_blank" >10.1007/978-3-642-33018-6_45</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Correlation-based Neural Gas for Visualizing Correlations between EEG Features

Popis výsledku v původním jazyce

Feature selection is an important issue in an automated data analysis. Unfortunately the majority of feature selection methods does not consider inner relationships between features. Furthermore existing methods are based on a prior knowledge of a data classification. Among many methods for displaying data structure there is an interest in self organizing maps and its modifications. Neural gas network has shown surprisingly good results when capturing the inner structure of data. Therefore we propose its modification (correlation - based neural gas) and we use this network to visualize correlations between features. We discuss the posibility to use this additional information for fully automated unsupervised feature selection where no classification isavailable. The algorithm is tested on the EEG data acquired during the mental rotation task.

Název v anglickém jazyce

Correlation-based Neural Gas for Visualizing Correlations between EEG Features

Popis výsledku anglicky

Feature selection is an important issue in an automated data analysis. Unfortunately the majority of feature selection methods does not consider inner relationships between features. Furthermore existing methods are based on a prior knowledge of a data classification. Among many methods for displaying data structure there is an interest in self organizing maps and its modifications. Neural gas network has shown surprisingly good results when capturing the inner structure of data. Therefore we propose its modification (correlation - based neural gas) and we use this network to visualize correlations between features. We discuss the posibility to use this additional information for fully automated unsupervised feature selection where no classification isavailable. The algorithm is tested on the EEG data acquired during the mental rotation task.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing

ISBN

978-3-642-33017-9

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

8

Strana od-do

439-446

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Ostrava

Datum konání akce

5. 9. 2012

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000312969500045