Automatic ocular artifact rejection based on independent component analysis and eyeblink detection

The presence of different kinds of artifacts has long been a problem in the analysis and interpretation of electroencephalographic recordings. Recently blind source separation by Independent Component Analysis (ICA) has been successfully employed for the detection and removal of artifactual components and new methods for the automatic identification of the artifactual components are being proposed. In this paper we focus on the automatic removal of eyeblink components from EEG data. First a model of the topographic maps associated to the ICA eyeblink component and a distance quantifying the resemblance to the model are defined. To further improve the reliability of the system, an eyeblink detector was designed which locates the individual eyeblinks within the component, thus confirming the nature of the activation.