Emotion Assessment Based on Functional Connectivity Variability and Relevance Analysis

The evaluation of emotional states has relevance in the development of systems that can automatically interact with human beings. The use of brain mapping techniques, e.g., electroencephalogram (EEG), improves the robustness of the emotion assessment methodologies in comparison to those schemes that use only audiovisual information. However, the high amount of data derived from EEG and the complex spatiotemporal relationships among channels impose several signal processing issues. Recently, functional connectivity (FC) approaches have emerged as an alternative to estimate brain connectivity patterns from EEG. Thereby, FC allows depicting the cognitive processes inside the human brain to support further brain activity discrimination stages. In this work, we propose an FC-based strategy to classify emotional states from EEG data. Our approach comprises a variability-based representation from three different FC measures, i.e., correlation, coherence, and mutual information, and a supervised kernel-based scheme to quantify the relevance of each measure. Thus, our proposal codes the inter-subject brain activity variability regarding FC representations. Obtained results on a public dataset show that the introduced strategy is competitive in comparison to state-of-the-art methods classifying arousal and valence emotional dimensional spaces.