Noise reduction methods have a great impact on the performance of all EEG signal processing systems. The work involves the reduction in impulsive interferences. It is impossible to suppress effectively such a noise using linear filtering approach. The work presents the properties of a cascaded filter based on the ordered weighted aggregation (OWA) operator and its application to improve the electroencephalogram (EEG) signal. The OWA operator is a class of mean-like aggregation operators. By introducing a nonlinear sorting process and by assigning appropriate values of weights to the sorted samples, the improvement in filtering in the impulsive environment is achieved. The structure of the proposed cascade consists of two layers. The first layer contains two OWA filters that share a certain number of signal samples. The second layer averages the outputs of the first layer. The algorithm for the rise of filtering efficiency has been introduced. The performance of the new method has been experimentally compared with the traditional methods of impulsive noise reduction, using synthetic as well as real signals from the CHB-MIT database. The obtained results demonstrate that in the field of impulsive noise suppression and preprocessing, the proposed cascaded OWA filter brings about a significant improvement in the signal-to-noise ratio.

The electrooculogram represents the electrical activity of muscles which steering of movements of an eye. The eye blinking is a natural protection system which defends the eye from environmental exposure. The spontaneous eye blink is considered to be a suitable indicator for fatigue diagnostics during many, different tasks of human being activity. The detection function is used to detect the spontaneous eye blink action. On this base the position of an eye blink is estimated. The results demonstrate that the measurement of an eye blink parameter provides reliable information for eye-controlled systems from human-machine interface.