A Case Study on the Removal of Blinking Artifact in Electroencephalogram Signals via Stochastic Filtering

The presence of physiological artifacts in electroencephalogram (EEG) signals is common and detrimental to experimental or clinical analysis. This paper presents a case study where we develop an algorithm based on stochastic filtering to remove blinking artifacts. For this case study, the dynamic system was defined by combining two autoregressive models, the first one represents the EEG signal, and the second one represents the blinking artifact. Some applications use stochastic methods to remove artifacts, so in this paper we apply the stochastic filtering via Kalman filter, that makes possible to remove blinking artifacts from single-channel EEG recordings, as long as the electrooculogram (EOG) signal was available. The method applies to a case study with actual artifacts and simulated artifacts for comparison. The measure of performance utilized is the estimated power spectral density (PSD). The results show that the proposed method could remove blinking artifacts without introducing significant distortions in the EEG signal.