Emotion Assessment Using Adaptive Learning-Based Relevance Analysis

The study of brain electrical activity (BEA) allows to describe and analyze the different cognitive and physiological process that occurs inside the human body. The Electroncephalogram (EEG) is often chosen over other neuroimaging techniques, but the non-stationarity nature of the EEG data and the variability between subjects have to be sorted to design reliable methodologies for neural activity identification. In this work, we propose the use of adaptive filtering for the relevance analysis of EEG segments in emotion assessment experiments. First, a windowing stage of the EEG data is performed, from which brain connectivity measures are extracted as BEA descriptors. The correlation and the time-series generalized measure of association (TGMA) are selected at this stage. Then, the connectivity data is used for galvanic skin response (GSR) and Blood Volume pressure (BVP) estimation employing the quantized kernel mean least squares (QKLMS) strategy. Finally, from the QKLMS algorithm, a set of relevant centroids in the estimation of physiological responses are used in the classification of the specific emotional state. The results obtained validate the proposed methodology and give clear evidence that a selection of segments from BEA improve further stages of classification for emotion assessment tasks.