Adaptive nonlinear projective filtering: Application to filtering of artifacts in EEG signals

In this work a novel approach to filtering of eyeblink related artifacts from EEG signals is presented. Proposed solution, the Adaptive Nonlinear Projective Filtering (ANPF) algorithm, combines the classic approach to adaptive filtering with algorithms from nonlinear state space projection family. Performance of described method is compared with adaptive filter based on Normalized Least Mean Squares algorithm in terms of median Normalized Mean Squared Error. Data used in conducted research was simulated according to described procedure. Such approach allowed for a reliable comparison and evaluation of algorithm's signal correction properties. Additionally, a real time modification of ANPF algorithm is proposed and tested. The analysis of sensitivity to changes of parameter values was also performed. Achieved results were tested for statistical significance. According to obtained scores ANPF significantly outperforms referential method during offline processing.