  EEG is a non-invasive technique for recording brain bioelectric activity,
which has potential applications in various fields such as human-computer
interaction and neuroscience. However, there are many difficulties in analyzing
EEG data, including its complex composition, low amplitude as well as low
signal-to-noise ratio. Some of the existing methods of analysis are based on
feature extraction and machine learning to differentiate the phase of
schizophrenia that samples belong to. However, medical research requires the
use of machine learning not only to give more accurate classification results,
but also to give the results that can be applied to pathological studies. The
main purpose of this study is to obtain the weight values as the representation
of influence of each frequency band on the classification of schizophrenia
phases on the basis of a more effective classification method using the LES
feature extraction, and then the weight values are processed and applied to
improve the accuracy of machine learning classification. We propose a method
called weight-voting to obtain the weights of sub-bands features by using
results of classification for voting to fit the actual categories of EEG data,
and using weights for reclassification. Through this method, we can first
obtain the influence of each band in distinguishing three schizophrenia phases,
and analyze the effect of band features on the risk of schizophrenia
contributing to the study of psychopathology. Our results show that there is a
high correlation between the change of weight of low gamma band and the
difference between HC, CHR and FES. If the features revised according to
weights are used for reclassification, the accuracy of result will be improved
compared with the original classifier, which confirms the role of the band
weight distribution.
