A method and system for detecting Internet addiction based on brain waves

Abstract

The invention discloses a method and system for detecting internet addiction based on brainwaves. Firstly, brainwave signals of different testers are collected, and the energy feature matrix of the brainwave signals after filtering is calculated; then, the energy feature matrix is ​​trained to obtain a well-trained The classification model; finally, the brain wave signal of the testee is input into the classification model, and the degree of Internet addiction of the testee is output. The present invention combines brain waves with Internet addiction detection, collects the brain wave information of teenagers with different levels of internet addiction after surfing the Internet, uses the ELM training classification model based on deep learning mapping kernel function, and uses this model to measure the degree of internet addiction of teenagers It is judged that the classification model has faster learning speed and stronger generalization ability than the traditional training model under the premise of ensuring the learning accuracy. It helps to improve the subjectivity of Internet addiction judgment in the past, and provides an objective and real auxiliary diagnosis plan for doctors to judge adolescent Internet addiction.

Description

technical field [0001] The invention belongs to the technical field of brain-computer interface auxiliary diagnosis, and relates to a method and system for detecting Internet addiction based on brain waves. Background technique [0002] Electroencephalogram (Electroencephalogram, EEG) is a method that uses electrophysiological indicators to record brain activity. When the brain is active, the synchronous post-synaptic potentials of a large number of neurons are summed and formed. Brain wave or EEG is a relatively sensitive objective indicator, which can not only be used in the basic theoretical research of brain science, but more importantly, its application in clinical practice is closely related to human life and health. However, its analysis is difficult, and it is not easy to directly find the correspondence between brain waves and actual patterns. Using machine learning principles, it is possible to accurately ignore common interference and discover special patterns tha...

AI Technical Summary

Problems solved by technology

However, the judgment of Internet addiction still depends on the time spent on the Internet, behavior observation or questionnaires, etc., which is highly subjective.

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