Unsupervised Eye Blink Artifact Identification in Electroencephalogram

The most prominent type of artifact contaminating electroencephalogram (EEG) signals is the eye blink (EB) artifact. Hence, EB artifact detection is one of the most crucial pre-processing step in EEG signal processing before this artifact can be removed. In this work, an approach that identifies EB artifacts without human supervision and automated varying threshold setting is proposed and evaluated. The algorithm functions on the basis of correlation between two EEG electrodes, Fp1 and Fp2, followed by EB artifact threshold determination utilizing the amplitude displacement from the mean. The proposed approach is validated and evaluated in terms of accuracy and error rate in detecting events of EB artifacts in EEG signals. Analysis has revealed that the proposed approach achieved an average of 96.6% accuracy compared to a conventional method of identifying EB artifacts with a fixed constant threshold.