Artifact suppression from EEG signals using data adaptive time domain filtering

This paper presents a data adaptive filtering approach to separate the electrooculograph (EOG) artifact from the recorded electroencephalograph (EEG) signal. Empirical mode decomposition (EMD) technique is used to implement the time domain filter. Fractional Gaussian noise (fGn) is used here as the reference signal to detect the distinguish feature of EOG signal to be used to separate from EEG. EMD is applied to the raw EEG and fGn separately to produce a finite number band limited signals named intrinsic mode functions (IMFs). The energies of individual IMFs of fGn and that of raw EEG are compared to derive the energy based threshold for the suppression of EOG effects. The separation results using EMD based approach is also compared with wavelet thresholding technique. The experimental results show that the data adaptive filtering technique performs better than the wavelet based approach.