Analysis of Depression Magnetoencephalogram Based on Multiscale Mutual Mode Entropy

In modern society, depression has become the fourth major disease affecting people’s physical and mental health. However, there is currently a lack of effective and quantifiable diagnostic methods for depression. This article uses the multiscale mutual mode entropy algorithm to analyze the magnetoencephalogram data of depression patients and normal people under different emotional picture stimuli, which combines the advantages of multiscale and mode entropy. The results show that the multiscale mutual mode entropy algorithm can effectively distinguish the magnetoencephalogram data of depression and health groups, and has the potential to become a reliable diagnostic method.