The paper presents an analysis of a mathematical model of the human arm dynamics in terms of observability. The purpose of the performed experiments is the selection of an observer for the possibility of arm tracking. The arm model is based on the two-link manipulator moving horizontally and vertically. For the study a model was linearized and the model part responsible for the work of human muscles was omitted. The experimental part involved simulated measurements of the motion parameters that imitate real-IMU (Inertial Measurement Unit) measurements. Finally, the simulation results using the observer in the form of a Kalman filter and the particle filter have been presented.

Eye movement related artifacts are the most significant source of noise in EEG signals. Thus, a special approach to reduction of their influence is required. However, most of currently used methods of detecting and filtering eye movement related artifacts require either an additional recording of noise signal, or are not suitable for real time applications, such as Brain-Computer Interfaces. In this paper it was proven that it is possible to detect and filter those artifacts in real time, without the need of providing an additional recording of noise signal.