Analysis Of Magnetoencephalography Based On Modified Permutation Entropy And Conditional Entropy

Depression is a kind of emotional disease.It can make people lack self-confidence,low mood,and can’t experience happiness in any interesting activities.Because the pathogenesis and clinical diagnosis of depression are complicated,and magnetoencephalography(MEG)is a non-invasive brain function detection technique,MEG has been widely used in the study of depression.The brain is a complex nonlinear dynamic system,and it is meaningful to study the magnetoencephalogram signal using complexity.In this paper,the permutation entropy,modified permutation entropy and conditional entropy are used to study the MEG signals of healthy samples and depression patients under different emotional image stimuli.Firstly,in the use of permutation entropy analysis of MEG data,the appropriate parameters are selected through experiments,the results show that the embedding dimension is the most obvious when the score is 4.then comparing the positive,neutral and negative emotions,the permutation entropy values of healthy samples and patients with depression found that the permutation entropy values of most people in healthy people were greater than those in depression,and the difference in frontal area was more obvious.The frontal area corresponds to the frontal lobes of the cerebral cortex.Its main function is to regulate human emotions.This result indicates that the ability of depression patients to adjust their emotions is still different from that of healthy people.Finally,the permutation entropy value of the same sample under different emotional stimuli was found that the permutation entropy value of patients with depression under positive emotional stimulation is greater than that under negative emotion stimulation,and the difference is obvious.Secondly,using the modified permutation entropy algorithm to study the MEG signal,the results show that under positive,neutral and negative emotional stimuli,the modified permutation entropy of mostly healthy people is greater than depression.Patients with symmetrical channels in the frontal area can distinguish two types of experimental objects well,and in the asymmetric channel of the brain,the right frontal area can better distinguish healthy samples from patients with depression.And the modified permutation entropy of healthy samples is larger than that of patients,indicating that the complexity of the brain of healthy people is higher than the complexity of patients.Thirdly,in the experiment of using the conditional entropy algorithm to study the MEG signal,the appropriate parameters are selected through experiments,and then the conditional entropy values of healthy people and depressed patients are calculated separately under positive,neutral and negative emotional stimuli.The conditional entropy of the same subject under three different emotional stimuli,the experimental results show that the conditional entropy of most channels of healthy people is larger than patients,and most of the channels of depression patients are positive.The conditional entropy value under emotional stimuli is greater than the conditional entropy value under neutral emotion stimuli,and the conditional entropy values under negative emotion stimuli are larger.The conditional entropy of all brain regions of healthy people is larger than patients,and the conditional entropy of two types of experimental subjects.The difference in the frontal area is most significant.