Data Adaptive Filtering Approach to Rhythmic Component Extraction from EEG Signal

Electroencephalography (EEG) signal collected from scalp surface is a non-invasive approach to study human brain activities. The rhythmic components of EEG signal illustrate the neural activities and effective to implement brain computer interface (BCI). This research presents an effective method of rhythmic component extraction (RCE) from multi-channel EEG. The proposed approach is based on multivariate empirical mode decomposition (MEMD). It decomposes multichannel EEG signal into a finite set of subband signals termed as intrinsic mode functions (IMFs). Such decomposition is fully data adaptive and effective for non-stationary signal. Each 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. The rhythmic component obtained from all the IMFs of the EEG channel are summed up yielding the channel’s rhythmic component. Therefore, majority of 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. To further improve the proposed method, the inter-channel correlation is taken into consideration during decomposition with MEMD hence it is very much effective for RCE from multichannel EEG signal.