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EEG is a non-invasive technique for recording brain bioelectric activity,which has potential applications in various fields such as human-computerinteraction and neuroscience. However, there are many difficulties in analyzingEEG data, including its complex composition, low amplitude as well as lowsignal-to-noise ratio. Some of the existing methods of analysis are based onfeature extraction and machine learning to differentiate the phase ofschizophrenia that samples belong to. However, medical research requires theuse of machine learning not only to give more accurate classification results,but also to give the results that can be applied to pathological studies. Themain purpose of this study is to obtain the weight values as the representationof influence of each frequency band on the classification of schizophreniaphases on the basis of a more effective classification method using the LESfeature extraction, and then the weight values are processed and applied toimprove the accuracy of machine learning classification. We propose a methodcalled weight-voting to obtain the weights of sub-bands features by usingresults of classification for voting to fit the actual categories of EEG data,and using weights for reclassification. Through this method, we can firstobtain the influence of each band in distinguishing three schizophrenia phases,and analyze the effect of band features on the risk of schizophreniacontributing to the study of psychopathology. Our results show that there is ahigh correlation between the change of weight of low gamma band and thedifference between HC, CHR and FES. If the features revised according toweights are used for reclassification, the accuracy of result will be improvedcompared with the original classifier, which confirms the role of the bandweight distribution.

Improvement of Resting-state EEG Analysis Process with Spectrum Weight-Voting based on LES