REFERENCE-FREE REDUCTION OF BALLISTOCARDIOGRAM ARTIFACT FROM EEG DATA USING EMD-PCA

Contemporaneous Electroencephalograph (EEG) and functional Magnetic Resonance
Image (fMRI) acquisitions provide a better insight to brain dynamics. Irrespective of
the fact that this modality fusion provides advantages, the artifacts pose a threat to
acquire a high quality of data. One such problematic artifact is the Ballistocardiogram
(BCG) artifact. The BCG artifact has a dynamic effect i.e. complex in spatial, nonstationary
in temporal and nonspecific signal in spectral domain. Despite several ef
forts to find an appropriate methodology for removing BCG artifact, significant inconsistency
exists between EEG-fMRI studies. This dissertation presents a referen.eefree
hybrid algorithm which combines features of Empirical Mode Decomposition
(EMD) with Principal Component Analysis (PCA) to reduce the BCG artifact. The
algorithm does not require the recordings of Electrocardiogram (ECG), Electrooculogram
(EOG) or the signals from motion sensors to extract the BCG artifact.