Automatic analysis method and system based on resting-state EEG frequency domain characteristics and brain network

Abstract

The invention discloses an automatic analysis method and system based on resting-state EEG frequency domain characteristics and a brain network, and the method comprises the steps: calling a Matlab data processing script, carrying out the preprocessing of the resting-state EEG of a patient in an eye-closed state for 3 minutes, calculating the frequency domain characteristics of the EEG, and further calculating and extracting the average brain network connection strength of different brain regions and the whole brain through the traceability positioning analysis. According to the method, the first 10% of strongest connections are visualized, and the two-dimensional electroencephalogram is quickly converted into high-readability digital and image information, so that automatic batch processing of electroencephalogram frequency domain characteristics and brain network analysis in scientific research work is facilitated, the working efficiency is improved, and the labor cost is saved. And on the other hand, the abnormal frequency domain and the brain region of the patient are accurately identified by comparing and identifying with a standardized reference value of a health contrast norm, and a powerful technical means is provided for realizing individualized and precise non-invasive nerve regulation and control.

Description

technical field [0001] The invention relates to the technical field of resting-state EEG rTMS diagnosis and treatment, in particular to an automatic analysis method and system based on resting-state EEG frequency domain characteristics and brain network. Background technique [0002] Electroencephalography (EEG) is a non-invasive, non-invasive neuroelectric signal acquisition technology, which can reflect the electrical activity of brain neuron cluster points with extremely high time accuracy, and it is time-consuming, expensive and costly compared with the examination. Compared with the original detection method, it has the advantages of high time resolution, short inspection time, low cost, and convenience. In recent years, EEG has become a powerful tool for assessing changes in brain functional activity. With the development of brain functional imaging technology, EEG signals are associated with neurological diseases such as epilepsy, cerebrovascular diseases, insomnia, a...

AI Technical Summary

Problems solved by technology

[0003] However, the frequency domain characteristics of EEG signals and the extraction of brain network analysis technology at the traceability level require familiarity with the software operation process of Matlab and EEGlab, and require analysts to have a certain background in engineering and computer science, which limits the application of EEG in medicine to a certain extent. Wide range of applications in the field

Secondly, the data volume of high-density EEG features is large, especially the analysis of EEG features at the traceability level, the processing time is relatively long, and the requirements for hardware and operators are relatively high. The current manual EEG processing mode limits the efficiency of data analysis

In addition, based on the frequency-domain characteristics of resting-state EEG and the results of brain network analysis, there is a lack of normative data of large-scale healthy population, that is, the normal reference range, and lack of interpretability, so it cannot be effectively applied in clinical practice; finally, the current brain network Feature analysis mostly uses machine learning algorithms such as deep learning, ignoring the important role of EEG features in different frequency bands and different brain regions in the underlying physiological mechanism of diseases

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