Mu rhythm desynchronization detection based on empirical mode decomposition.

The aim of this paper is to investigate the possibility of using empirical mode decomposition (EMD) method in detecting the desynchronized mu rhythm of motor imagery EEG signal. A number of EEG studies have identified the mu rhythm desynchronization a reliable EEG pattern for brain-computer interface. Considering the non-stationary characteristics of the motor imagery EEG, the EMD method is proposed to decompose the EEG signal into intrinsic mode functions (IMFs). By analyzing the power spectral density (PSD) of the IMFs, the characteristics one representing mu rhythm oscillations can be detected. Then by Hilbert transformation, the event-related desynchronization phenomenon can be found by the envelope of the characteristics IMF. Results demonstrate that the EMD method is an effective time-frequency analysis tool for non-stationary EEG signal.