Adaptive Technique for the Minimization of EOG Artefacts from EEG Signals Using TDL Structure and Nonlinear, Estimation Model

EEG records are often contaminated with extracerebral signals called artefacts and one of the main disturbances is due to eye movements which generate an electrical activity called EOG. In this paper, we use an adaptive noise cancellation scheme in a novel way for the minimization of the EOG artefacts from corrupted EEG signals. This method is based on the fact that the transfer function of the biological neuron can be modelled as a sigmoidal nonlinearity. Comparison of the time plots as also the smoothed linear prediction specta show that the proposed method effectively minimize the EOG artefacts from corrupted EEG signals