A new approach for ocular artifact removal from EEG signal using EEMD and SCICA. Issue 1 (1st January 2020) - Yadav, Anchal; Choudhry, Mahipal Singh
Abstract: EEG data obtained from the scalp using the electrodes, usually gets contaminated by various artifacts like muscle artifact, line interference artifact, ocular artifact, and others. The Ocular artifact which is caused due to eye-blink or other eye movements, while measuring EEG is the most common and most critical EEG artifact. For a long time, it has been a research challenge for any method to remove the ocular artifact from EEG without causing loss of the EEG signal. In this paper, a new approach is introduced to remove the ocular artifact from EEG signal using Ensemble Empirical Mode Decomposition (EEMD) and Spatial Constraint Independent Component Analysis (SCICA) without causing loss to EEG signal. The contribution of the method lies in the fact that it combines the advantages of both EEMD and SCICA. Here, EEMD is applied to the artifactual EEG signal to obtain Implicit Mode Functions (IMFs). The artifactual IMFs are separated from artifact-free IMFs by using the Correlation Coefficient-based algorithm. Now, the artifactual IMFs are provided as the input channels to Independent Component Analysis (ICA) and to obtain Independent Components (ICs) and inverse mixing matrix. Then, the Kurtosis and mMSE techniques are used to define the threshold levels for differentiating between artifactual and artifact-free ICs. Moreover, the mixing matrix is also modified using spatial constraints. The modified mixing matrix and ICs are then used to obtain restored IMFs. Finally, the artifact-free EEG signal is reconstructed by summing up artifact-free IMFs and restored IMFs. The proposed method is compared with other state-of-the-art methods in terms of Mutual Information, Correlation Coefficient, and Coherence. The results show that the proposed method has better performance as compared to other state-of-the-art methods for ocular artifact removal from EEG.