Independent Component Analysis for EOG artifacts minimization of EEG signals using kurtosis as a threshold

Brain electrical activity commonly represented by the Electroencephalogram (EEG), due to its miniscule amplitude (on the order of a hundred microvolts), is often contaminated with various artifacts. Independent Component Analysis (ICA) may be a useful technique to minimize the artifacts prior analyzing the original neural signal. In this paper, we used kurtosis to determine the threshold to separate the artifacts-affected ICA components from the unaffected components. Kurtosis may represent how peaked or how flat the artifacts that affect a signal are compared to the normal behavior of the original signal. To select the threshold value of the kurtosis, two statistical principles have been used: namely, the Z-score and the confidence interval. Our intention was to avoid a manual technique to determine the affected ICA components and, instead, to explore an automatic method based on the kurtosis value. Based on the observed results, we may conclude that the present technique may be used for EOG artifacts minimization.