Correlation-based Neural Gas for Visualizing Correlations between EEG Features

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Correlation-based Neural Gas for Visualizing Correlations between EEG Features
Original language description
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.
Czech name
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Czech description
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Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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