A non-linear estimation model for adaptive minimization of EOG artefacts from EEG signals

In this paper, we propose an adaptive noise cancellation scheme in a novel way for the minimization of electrooculogram (EOG) artefacts from corrupted EEG signals. This method is based on the fact that the transfer function of the biological neuron can be modeled as a sigmoid non-linearity. Comparison of the time plots and the smoothed linear prediction spectra show that the proposed method effectively minimizes the EOG artefacts from corrupted EEG signals. We have also studied the performance of the above scheme for different values of filter order (P) and the convergence factor (mu). Normalised Mean Squared Error (NMSE) has been used as the measure for comparison. The study shows that the NMSE decreases with increase in P and mu (but saturates after certain values of the parameters), thereby implying a better EOG minimization from EEG signals. It is also observed that the EOG minimization scheme with two EOG reference inputs works better than that with one reference input.