EOG DENOISING USING EMPIRICAL MODE DECOMPOSITION AND DETRENDED FLUCTUATION ANALYSIS


Mert A., Akkurt N., Akan A.

22nd IEEE Signal Processing and Communications Applications Conference (SIU), Trabzon, Türkiye, 23 - 25 Nisan 2014, ss.544-547 identifier identifier

  • Cilt numarası:
  • Doi Numarası: 10.1109/siu.2014.6830286
  • Basıldığı Şehir: Trabzon
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.544-547

Özet

In this study, a method is presented for the removal of electrooculogram (EOG) noise from electroencephalography (EEG) recordings by using recently proposed data driven approach called Empirical Mode Decomposition (EMD). The EMD represents the signal as a combination of Intrinsic Mode Functions (IMFs). It is an important problem to determine which IMFs belong to signal and noise in multi-component or noisy signals. Detrended Fluctuation Analysis (DFA) is a successful method to characterize non-stationary signals. In our approach, a threshold is determined from the DFA, and used to select the noise IMEs. Performance of the proposed method is demonstrated by means of computer simulations using noisy EEG signals.