EEG-fMRI-Based Multimodal Fusion System for Analyzing Human Somatosensory Network

Pain perception in the brain is inherently subjective, with the existing quantitative measures of somatosensory sensitivity in healthy adults largely confined to results from subjective behavioral surveys. In this study, we used an unsupervised K-mean algorithm to cluster the somatosensory sensitivities of healthy adults to different stimuli, and the Gaussian classifier and K-nearest neighbor classifier for 75 participants achieved peak accuracy of 0.982 and 0.924 for K-means with ${k}={4}$. Using Multidimensional scaling (MDS) to perform confirmation of the cluster distribution relationships for the four types of generally hypersensitive (HS), generally non-sensitive (NS), predominantly thermally sensitive (TS), and predominantly mechanically sensitive (MS). The investigation and quantification of somatosensory stimulus types in healthy adults will bring a deeper understanding of the breadth and applicability of cognitive neuroscience. The effect of data fusion benefits was achieved by using the EEG and the precise spatial localization of MRI to investigate the connectivity coherence of functional brain networks across different somatosensory phenotypes. We found that the number of brain regions activated in the TS type has a maximum of 43 brain regions and NS type has a minimum of 31 brain regions.