A Hybrid Method to Improve the Reduction of Ballistocardiogram Artifact from EEG Data

Simultaneous recordings of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) allow acquisition of brain data with high spatial and temporal resolution. However, the EEG data get contaminated by additional artifacts such as Gradient artifact and Ballistocardiogram (BCG) artifact. The BCG artifact’s dynamics appear to be more challenging and it hinders in the assessment of the neuronal activities. In this paper, a referencefree method is implemented in which Empirical Mode Decomposition (EMD) and Principal Component Analysis (PCA) has been combined to reduce the BCG artifact while preserving the neuronal activities. The qualitative analysis of the proposed method along with three existing methods demonstrates that the proposed method has improved the quality of the reconstructed data. Moreover, it does not require any reference signal to extract BCG artifact.