An efficient approach for automatic removal of ocular artifacts from multichannel EEG data

This paper proposes an efficient approach, based on independent component analysis (ICA) and wavelet denoising (WD), for automatic removal of ocular artifacts from EEG data. In the proposed approach, we employ the concept of spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the EEG data, use WD to remove any cerebral activity from extracted ICs to obtain artifact-only components, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as it is not necessary to identify all ICs. Computer experiments are carried out for long term EEG, which demonstrate the effectiveness of the proposed approach.