Abstract
Electroencephalography (EEG) signal has been widely used to recognize the intention and cognition in brain computer interface (BCI) applications. Non-Cerebral signals like electro-oculogram (EOG) artifacts with higher energy suppress important low energy EEG components. It is required to completely remove these artifacts without loss of EEG data. This paper presents multivariate empirical mode decomposition (MEMD) based filtering approach to suppress the unwanted artifacts. The multichannel EEG signal is decomposed into a finite number of band limited signals called intrinsic mode functions (IMFs). A subband thresholding is implemented to separate artifacts using the IMFs with fractional Gaussian noise (fGn) as reference signal. Thus separated artifact includes some EEG components. Bandpass filtering is implemented to extract the EEG signal from artifact obtained by energy based thresholding. The experimental results conducted with real EEG signals show the effectiveness of the proposed method.
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