Method for detecting internet addiction based on brain wave and system thereof

Abstract

The invention discloses a method for detecting internet addiction based on brain wave and a system thereof. The method comprises the following steps: brain wave signals of different test subjects arecollected, and an energy characteristic matrix of the brain wave signal after filtering is calculated; then the energy feature matrix is trained to obtain a trained classification model; finally, thebrain wave signal of the test subject is input into a classification model, and the degree of internet addiction of the test subject is output. The method combines brain waves with internet addictiondetection, and collects brain wave information of teenagers after surfing the Internet with different degrees of internet addiction, and uses an ELM training classification model based on a deep learning mapping kernel function, The model is used to judge the degree of internet addiction among teenagers. The classification model has faster learning speed and stronger generalization ability than the traditional training model under the premise of ensuring learning accuracy. The method and the system can help to improve the subjectivity of the past Internet addiction judgment, and provide an objective and true auxiliary diagnosis program for doctors to judge Internet addiction of the teenagers.

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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