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Magnetoencephalography (MEG) is a kind of brain function detection technology without trauma, which detects the electromagnetic physiological signal in the brain. It is widely used in depression research. We used permutation entropy, improved permutation entropy, conditional entropy to study the difference of MEG between depressed patients and healthy people, respectively. We implement three different entropy algorithms and compare the relative differences of entropy values in different brain regions of healthy people and depression patients under positive, neutral and negative emotional stimuli. The experimental results show that the relative difference of conditional entropy &gt; the relative difference of improved permutation entropy &gt; the relative difference of permutation entropy, and the difference of entropy value in the frontal region of human brain is the most obvious, which may provide the basis for the diagnosis of depression.

Study on nonlinear analysis of MEG in patients with brain diseases