Extracting Visual Evoked Potential via a Novel Internal Model Adaptive Kalman Filtering Approach

Based on the internal models of the deterministic signal or disturbance, the mixture of the visual evoked potential (VEP) and residual electro-encephalogram (EEG) is modeled by a set of augmented state-space equations. Then, an internal model adaptive kalman filtering (IMAKF) based on the non-linear extended kalman filtering iterative (EKF) algorithm is utilized for extracting the VEP with the EEG as an on-going biological background noise. Numerical simulation and clinical application have shown the effectiveness of the proposed approach.