Research And Visualization Of Brain Function Network Connectivity For Cognitive Functional Analysis

The analysis and visualization of functional connectivity networks(FCN)based on electroencephalography(EEG)can provide many intuitive and useful information for studying the information transmission and collaboration between different brain regions of the human brain and exploring changes in cognitive function of the brain.When obtaining FCN through existing functional connectivity analysis indicators,the impact of key nodes(also known as central nodes,which play a key role in information transmission)in FCN is often ignored,resulting in visual confusion when visualizing FCN.In addition,most of the existing FCN visualization software is used as a tool kit for auxiliary operation,and the visualization effect is usually a static plane.In addition,when analyzing FCNs,it is usually necessary to reduce their dimensions into onedimensional long vectors,which will lose the network topology of the FCN itself.In response to the above issues,the research content of this article mainly includes the following three points:(1)Aiming at the problem of visual confusion caused by ignoring key nodes when using existing functional connectivity analysis indicators to obtain FCNs,a new functional connectivity analysis indicator Comprehensive is proposed.This indicator effectively eliminates visual confusion during visualization by calculating the access degree of each channel as a weight,traversing and adjusting the connectivity results between all channels in the entire FCN.(2)Aiming at the shortcomings of existing FCN visualization software,an EEGbased functional connectivity visualization(EEG-FCV)software was designed.The software can run independently,visualize FCN in 3D,and display the temporal dynamic changes of FCN through a sliding time window.(3)To solve the problem of losing network topology during FCN analysis,a robust principal component analysis(RPCA)based multiple clustering algorithms(RPCA MCA)was proposed.This algorithm effectively extracts low rank sparse components from FCNs through RPCA,preserving the network topology of FCNs,and then clusters them using different clustering methods to achieve cross subject FCN clustering analysis.Finally,3D visualization of clustering results is performed using FCN-based cluster visualization(FCN-CV)software.Finally,validation was conducted on emotional data sets,fatigue driving data sets,and epilepsy data sets,and consistent conclusions were obtained with existing research,effectively proving the effectiveness and accuracy of the work of this article,which can provide important assistance for the analysis and research of brain cognitive function.