Common Methodology for Cardiac and Ocular Artifact Suppression from EEG Recordings by Combining Ensemble Empirical Mode Decomposition with Regression Approach.

Electroencephalography (EEG) is a non-invasive way of recording brain activities, making it useful for diagnosing various neurological disorders. However, artifact signals associated with eye blinks or the heart spread across the scalp, contaminating EEG recordings and making EEG data analysis difficult. To solve this problem, we implement a common methodology to suppress both cardiac and ocular artifact signal, by correlating the measured contaminated EEG signals with the clean reference electro-oculography (EOG) and electrocardiography (EKG) data and subtracting the scaled EOG and EKG from the contaminated EEG recording. In the proposed methodology, the clean EOG and EKG signals are extracted by subjecting the raw reference time-series data to ensemble empirical mode decomposition to obtain the intrinsic mode functions. Then, an unsupervised technique is used to capture the artifact components. We compare the distortion introduced into the brain signal after artifact suppression using the proposed method with those obtained using conventional regression alone and with a wavelet-based approach. The results show that the proposed method outperforms the other techniques, with an additional advantage of being a common methodology for the suppression of two types of artifact.