EEG eye blink artifact removal by EOG modeling and Kalman filter

We present a novel method to remove eye blink artifacts from the electroencephalogram (EEG) signals, without using electro-oculogram (EOG) reference electrodes. We first model EEG activity by an autoregressive model and eye blink by an output-error model, and then use Kalman filter to estimate the true EEG based on integrating two models. The performance of the proposed method is evaluated based on two different metrics by using Dataset IIa of BCI competition 2008. For RLS algorithm, artifact removal and EEG distortion metrics are 7.35 and 0.79, while for our proposed method these metrics are 9.53 and 0.84, respectively. The results show that our proposed method removes the EOG artifact more efficiently than RLS algorithm. However, the RLS algorithm causes a little less EEG signal distortion.