Residual dependency estimation of independent components applied to EEG event related potentials associated with emotional processing

Independent component analysis can be employed as an exploratory method in electroencephalographic (EEG) data analysis. However, the assumption of statistical independence among the estimated components is not always fulfilled by ICA-based numerical methods. Furthermore it may happen that one physiological source can be split in two or more components. As a consequence, the estimated components must be further investigated to assess the existence of reciprocal similarities. In this work a method for finding residual dependency subsets of component is proposed. Firstly a hierarchical clustering stage is carried out to classify ICA results. Then the hierarchical tree is investigated at each level by two indices to evaluate the tightness of all clusters. At the same time clustered scalp projections are compared with a template, which is shaped by applying ensemble ICA to a training dataset. Results are shown on EEG data acquired in event-related brain potentials (ERPs) studies for emotional pictures processing. In this kind of experiment ERPs are measured whilst unpleasant and neutral images are shown to a subject. The clustering procedure and the performance indices succeeded in isolating compact groups of components. These components, taken together, reflect the brain's biopotentials related to emotional processing at different cortical areas.