Pregled bibliografske jedinice broj: 701077
Hilbert-Huang time-frequency analysis of motor imagery EEG data for brain-computer interfaces
Hilbert-Huang time-frequency analysis of motor imagery EEG data for brain-computer interfaces // 6th European Conference of the International Federation for Medical and Biological Engineering: IFMBE Proceedings 45 / Lacković, Igor ; Vasić, Darko (ur.).
Dubrovnik, Hrvatska: Springer, 2014. str. 62-65 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 701077 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Hilbert-Huang time-frequency analysis of motor imagery EEG data for brain-computer interfaces
Autori
Jerbić, Ana Branka ; Horki, Petar ; Sovilj, Siniša ; Išgum, Velimir ; Cifrek, Mario
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
6th European Conference of the International Federation for Medical and Biological Engineering: IFMBE Proceedings 45
/ Lacković, Igor ; Vasić, Darko - : Springer, 2014, 62-65
Skup
European Conference of the International Federation for Medical and Biological Engineering (6 ; 2014)
Mjesto i datum
Dubrovnik, Hrvatska, 07.09.2014. - 11.09.2014
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
brain-computer interface (BCI); motor imagery (MI); time-frequency (TF) analysis; Hilbert-Huang transform (HHT)
Sažetak
Brain-computer interface (BCI) is a technology that provides a non-muscular communication channel between a brain and the outside world. Imagination of left and right hand movements results in spatially distinct brain activation patterns that can be used as control signals for the BCI. Motor imagery (MI) results in the attenuation (event related desynchronization, ERD) or enhancement (event related synchronization, ERS) of amplitude in a certain frequency band of electroencephalogram (EEG). This frequency band can vary between different participants. Therefore time-frequency (TF) analysis is performed in order to extract interesting features from EEG. A simple way of performing TF analysis is by using band power features. In this paper we investigate the perspective of Hilbert-Huang transform (HHT) for extracting TF information used for MI classification. HHT is a method that allows calculation of instantaneous frequency and amplitude of the signal. It does that by decomposing the signal into components for which these parameters can be calculated by means of Hilbert transform. We compare classification accuracy of simple band power features and features obtained by means of HHT on BCI competition IV dataset 2b.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne medicinske znanosti
POVEZANOST RADA
Projekti:
312-0362979-3258 - ISTRAŽIVANJE NEUROFIZIOLOGIJE POKRETA PRIMJENOM METODE EVOCIRANIH POTENCIJALA (Išgum, Velimir, MZOS ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Sveučilište u Zagrebu
