On the Most Informative Slice of Bicoherence That Characterizes Resting State Brain Connectivity

Bicoherence is a useful tool to detect nonlinear interactions within the brain with high computational cost. Latest attempts to reduce this computational cost suggest calculating a particular ‘slice’ of the bicoherence matrix. In this study, we investigate the information content of the bicoherence matrix in resting state. We use publicly available Human Connectome Project data in our calculations. We show that the most prominent information of the bicoherence matrix is concentrated on the main diagonal, i.e. $f_{1}=f_{2}$