Measuring the Complexity of Consciousness
arXiv Preprint Archive January 11, 2018 Xerxes D. Arsiwalla, Paul Verschure
A new framework using information-theoretic complexity measures, such as integrated information, has been proposed to quantitatively classify states of consciousness, addressing both phenomenological contents and clinical disorders. However, applying these measures to realistic brain networks is difficult due to high computational costs. This article serves as a lookup table of principle-based and empirically tested measures of consciousness, with emphasis on clinical applicability for assisting diagnosis and therapy. It addresses challenges facing these measures with regard to realistic brain networks and suggests possible resolutions.