Method for extracting features of neurosis based on graph theory and machine learning
A machine learning and feature extraction technology, applied in the fields of instruments, sensors, medical science, etc., can solve problems such as abnormal interaction and coordination, functional impairment of diseases, and inability to effectively solve common source problems.
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[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the specific implementation manners of the present invention will be further described in detail below.
[0019] Examples of the present invention provide a neurosis feature extraction method based on graph theory and machine learning, see figure 2 and figure 2 , the method includes brain functional network construction based on EEG data, topological feature extraction and recognition, and brain functional network connection localization in the somatic and emotional dimensions of neurosis.
[0020] Step 1: Acquisition of EEG data
[0021] The collection of EEG data is completed in an electro-acoustic shielding room with noise less than 20dB, which can isolate electromagnetic waves and AC conduction interference / AM / FM radio wave interference. When collecting signals, REF is the default reference electrode, GND is the ground electrode, and the remaining 62 electrodes are ...
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