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

Proposes 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 nonlinearity. 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 the filter order (P) and the convergence factor (μ). The normalised mean squared error (NMSE) has been used as the measure for comparison. The study shows that the NMSE decreases with increasing P and μ (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