Rhythmic Component Extraction from EEG Signal Using Data Adaptive Multiband Filtering

The rhythmic components of electroencephalography (EEG) signal illustrate the neural activities and effective to implement brain computer interface (BCI). This paper presents an efficient rhythmic component extraction (RCE) method from multi-channel EEG. It is based multivariate empirical mode decomposition (MEMD) which decomposes multichannel EEG signal into a finite set of subbands termed as intrinsic mode functions (IMFs). The decomposition is fully data adaptive and effective for non-stationary signal. The IMF is a time varying band limited signal. It is filtered using a Fourier transform based zero phase bandpass filter for a specific rhythmic component. Thus obtained rhythmic component of all IMFs of an EEG channel are summed up yielding the channel’s component. All the desired components of individual channels are extracted using the same method. The energies of different extracted rhythmic components are compared as a function of channels. The inter-channel correlation is taken into consideration during decomposition with MEMD hence it is very much effective for RCE from multichannel EEG signal.