Background: Newest insights in the field of brain-information coding suggest that the information is transferred between the active regions of the brain as a phase-coded content. Considering the informational richness of the electroencephalographic (EEG) signals, we can assume that by using appropriate methods of signal processing it is possible to decode some of this information. The authors would like to show that using a phase-demodulation approach it is possible to successfully decode the information about the wrist movements of a complex dynamic visuo-motor task (dVM). Since the causality of the methodology is assured, it is also usable for the development of a brain-computer interface (BCI). Methods: In this study we measured the EEG signals from four subjects while performing a dynamic visuo-motor task. For decoding the information, which is supposedly carried by the EEG signals we used brain-rhythm filtering, phase demodulation and principal component analysis approach. As a prediction model for wrist movements, fuzzy inference model was used. Results: The presented results show that the EEG signals measured during the performance of dVM tasks carry enough information about the current action for satisfactory decoding and prediction of the wrist movements. Successful estimation of the motor action is proved also by obtaining reasonably high values of the correlation coefficients. Conclusions: The study has shown that using the proposed methodology it is possible to decode the EEG information of the wrist movements during dVM tasks. The study has also shown that these relatively simple methods of signal processing and a fuzzy model are applicable to the development of a closed-loop, non-invasive BCI.