Depression risk screening system and method based on virtual reality scene electroencephalogram signal

Abstract

The invention provides a depression risk screening system and method based on a virtual reality scene electroencephalogram signal. Under the stimulation of a virtual reality scene, real-time acquisition, processing and analysis of data are carried out on the electroencephalogram signal by an electroencephalogram acquisition system, and finally, a depression risk is screened by a data mining method. The system provided by the invention comprises a virtual reality induction system, a electroencephalogram signal acquisition system and a data analysis system; the virtual reality induction system is used for establishing different virtual reality scenes; the electroencephalogram signal acquisition system is used for acquiring the electroencephalogram signal of a human brain, which is generated under the stimulation of the virtual reality scene and is continuously changed, and outputting the processed electroencephalogram signal and extracted real-time feature information; the data analysis system has a classification discrimination model trained by labeled healthy populations and depression risk populations, compares feature information required in a depression screening process with feature parameters of the classification discrimination model, and distinguishes and discriminates the healthy populations and the depression risk populations.

Description

technical field [0001] The invention relates to the technical field of computer-aided medical diagnosis, in particular to a depression risk screening system and method based on EEG signals in a virtual reality scene. Background technique [0002] 1. Technical background information related to EEG signal acquisition [0003] EEG signals are collected from the epidermis of the human head, and are closely related to brain activity and emotional state. Existing studies have shown that EEG signals can reflect human emotional changes in real time. The study of EEG signals can be applied to understand the mechanism of brain activity, human cognitive process and diagnosis of brain diseases, as well as the field of brain-computer interface that has attracted much attention in recent years. [0004] EEG signal is a spontaneous potential activity generated by brain nerve activity and always exists in the central nervous system. It is an important bioelectrical signal. Resting-state ...

AI Technical Summary

Problems solved by technology

[0021] 4. Existing technologies and shortcomings of depression risk screening and diagnosis

[0023] (1) There are few depression screening programs and single means: most of the depression screening methods are based on the patient's hormone levels and various biochemical indicators as screening reference indicators, and there is a lack of generalized depression screening methods on the market Compared with the corresponding scheme, the index parameters of bioelectrical signals are lacking, which makes the method of screening for depression have certain limitations;

[0024] (2) EEG signal acquisition equipment is not universal: medical EEG signal acquisition equipment is complex and expensive, and requires a special person to be responsible for acquisition; portable EEG acquisition equipment, the number and location of EEG acquisition electrodes are uncertain, EEG acquisition electrodes and data transmission methods are different, as well as cost and application fields. The most important thing is that the power consumption is relatively large, it cannot be used continuously, and the number of A / D conversion bits is low;

[0025] (3) The traditional stimulation scheme does not have good implementability: the traditional stimulation methods are mostly pictures, music, and 2D videos, which cannot attract the attention of the subjects well

Poor system experience, lack of immersion, lack of interaction with patients, can not guide patients to use it well, is easily affected by the environment, affects the screening effect, resulting in a high dropout rate of patients;

[0026] (4) Data modeling and analysis lack relative reliability: the modeling data is small, and the amount of information contained is small, so that the data model does not have good balance and effectiveness

The EEG extraction algorithm is not good enough to obtain pure physiological EEG signals

A single data analysis method makes the results of feature extraction and selection lack representativeness and accuracy

These shortcomings make it impossible to give rationalized depression risk results for the subject's characteristic information

[0027] At present, there are no related reports on the depression screening system based on generalized EEG signals under the stimulation of virtual reality scenes

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