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Pregled bibliografske jedinice broj: 630477

Feature extraction from electroencephalographic records using EEGFrame framework


Jović, Alan; Suć, Lea; Bogunović, Nikola
Feature extraction from electroencephalographic records using EEGFrame framework // Proceedings of 36th International Convention MIPRO 2013 / Biljanović, Petar (ur.).
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2013. str. 1237-1242 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 630477 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Feature extraction from electroencephalographic records using EEGFrame framework

Autori
Jović, Alan ; Suć, Lea ; Bogunović, Nikola

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of 36th International Convention MIPRO 2013 / Biljanović, Petar - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2013, 1237-1242

ISBN
978-953-233-074-8

Skup
MIPRO 2013

Mjesto i datum
Opatija, Hrvatska, 20.05.2013. - 24.05.2013

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
feature extraction; biomedical time-series; data mining; electroencephalogram; Java; framework

Sažetak
Analysis of electroencephalographic (EEG)signals usually includes visual inspection of the signal, feature extraction, and model generation. Computer-aided nonlinear feature extraction from EEG in particular has already led to improved descriptive and prognostic models of brain states and disorders. However, in this field, there is a lack of freely available powerful tools for scientific exploration of EEG that would help researchers to compare the results of their work with others. Especially, because of the great diversity of the proposed methods for EEG analysis, there exists a need for a joint framework for inspection, extraction and visualization performed on the EEG records. The aim of this paper is to introduce such a framework, called EEGFrame, with its implementation in Java. The framework currently supports the analysis of standard EDF records via signal inspection, feature extraction, and feature vectors storage for knowledge discovery. EEGFrame is the result of refactoring and extension of the HRVFrame framework for heart rate variability analysis, with added methods for EEG analysis. This paper describes the properties and capabilities of the framework and discusses its relevance with respect to similar work. The main advantage of EEGFrame is its support for numerous linear and nonlinear methods described in literature.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Projekti:
036-0362980-1921 - Računalne okoline za sveprisutne raspodijeljene sustave (Srbljić, Siniša, MZO ) ( CroRIS)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Nikola Bogunović (autor)

Avatar Url Alan Jović (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada

Citiraj ovu publikaciju:

Jović, Alan; Suć, Lea; Bogunović, Nikola
Feature extraction from electroencephalographic records using EEGFrame framework // Proceedings of 36th International Convention MIPRO 2013 / Biljanović, Petar (ur.).
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2013. str. 1237-1242 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Jović, A., Suć, L. & Bogunović, N. (2013) Feature extraction from electroencephalographic records using EEGFrame framework. U: Biljanović, P. (ur.)Proceedings of 36th International Convention MIPRO 2013.
@article{article, author = {Jovi\'{c}, Alan and Su\'{c}, Lea and Bogunovi\'{c}, Nikola}, editor = {Biljanovi\'{c}, P.}, year = {2013}, pages = {1237-1242}, keywords = {feature extraction, biomedical time-series, data mining, electroencephalogram, Java, framework}, isbn = {978-953-233-074-8}, title = {Feature extraction from electroencephalographic records using EEGFrame framework}, keyword = {feature extraction, biomedical time-series, data mining, electroencephalogram, Java, framework}, publisher = {Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO}, publisherplace = {Opatija, Hrvatska} }
@article{article, author = {Jovi\'{c}, Alan and Su\'{c}, Lea and Bogunovi\'{c}, Nikola}, editor = {Biljanovi\'{c}, P.}, year = {2013}, pages = {1237-1242}, keywords = {feature extraction, biomedical time-series, data mining, electroencephalogram, Java, framework}, isbn = {978-953-233-074-8}, title = {Feature extraction from electroencephalographic records using EEGFrame framework}, keyword = {feature extraction, biomedical time-series, data mining, electroencephalogram, Java, framework}, publisher = {Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO}, publisherplace = {Opatija, Hrvatska} }




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