Separation of EEG signals by using independent component analysis
Küçük Resim Yok
Tarih
2012
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Independent Component Analysis (ICA) is a statistical method used for separating nongaussian independent components of a mixture signal. In this study, by separating the signal into its possible independent components, the simplification and comprehension of analysis of EEG signals was aimed. Through such an analysis it was thought that early diagnosis of some neurological disease such as epilepsy, parkinson's disease, sleep disorders as well as information regarding the location and size of problematic zone may become possible. © 2012 IEEE.
Açıklama
2012 20th Signal Processing and Communications Applications Conference, SIU 2012 -- 18 April 2012 through 20 April 2012 -- Fethiye, Mugla -- 90786
Anahtar Kelimeler
EEG, FastICA, Independent component analysis, Kurtosis
Kaynak
2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A