Detection of signaling pathways in human brain during arousal of specific emotion

Neuroscientists usually determine similarity between EEG electrode signals, by a measure of pairwise linear dependence among them. However, recent research indicates the drawbacks of analyzing the pairwise dependence of signals instead of analyzing the simultaneous joint interdependence among them. To overcome this problem we propose a novel similarity measure known as probabilistic relative correlation. Our approach is unique because our similarity measure allows the electrodes to have probabilistic similarity measures and recognizes emotion dependent structures even from mismatched sequences of correlation. We further validate our proposed similarity measure by testing it on the well-known emotion recognition problem. Our experiments have noteworthy implications towards realizing the neural signatures of discrete emotions and will allow for the better understanding of neurological pathways associated with different emotional states. To identify the most active neurological pathways in brain during an emotion, we adapt the minimal spanning tree algorithm.