Feature analysis of EEG signals using SOM

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F11%3A43882847" target="_blank" >RIV/60461373:22340/11:43882847 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Feature analysis of EEG signals using SOM

Original language description

The electroencephalogram (EEG) represents an e?cient technique to measure and record brain electrical activity. The most common use of EEG includes the monitoring and diagnosis of the brain states and their disorders. It is based on the search of characteristic patterns in EEG signals and their evaluation. In terms of signal processing it uses feature analysis, more speci?cally feature extraction and classi?cation of signal components. The paper deals with the feature study of EEG signals by the self-organizing neural network (SOM). The SOM is an unsupervised method using a neighborhood function to preserve the topological properties of the input space. Resulting algorithm was implemented in MATLAB with many optional parameters that provide possibilityto adjust the model to user's equirements. The graphical user interface was designed as well. General problems of feature analysis, such as extraction of appropriate characteristic features or evaluation of quality of clusters, were also

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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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

2011

Confidentiality

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

Name of the periodical

Posterus

ISSN

1338-0087

e-ISSN

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Volume of the periodical

4

Issue of the periodical within the volume

2

Country of publishing house

SK - SLOVAKIA

Number of pages

7

Pages from-to

1-7

UT code for WoS article

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EID of the result in the Scopus database

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