Pregled bibliografske jedinice broj: 630477
Feature extraction from electroencephalographic records using EEGFrame framework
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