utilizing normalized mutual information as a similarity measure for eeg and fmri fusion

Background and objectives: neuroscience research can benefit greatly from the fusion of simultaneous recordings of electroencephalogram (eeg) and functional magnetic resonance imaging (fmri) data due to their complementary properties. we can extract shared information by coupling two modalities in a symmetric data fusion.methods: this paper proposed an approach based on the advanced coupled matrix tensor factorization (acmtf) method for analyzing simultaneous eeg-fmri data. to alleviate the strict equality assumption of shared factors in the common dimension of the acmtf, the proposed method used a similarity criterion based on normalized mutual information (nmi). this similarity criterion effectively revealed the underlying relationships between the modalities, resulting in more accurate factorization results.results: the suggested method was utilized on simulated data with various levels of correlation between the components of the two modalities. despite different noise levels, the average match score improved compared to the acmtf model, as demonstrated by the results.conclusion: by relaxing the strict equality assumption, we can identify shared components in a common mode and extract shared components with higher performance than the traditional methods. the suggested method offers a more robust and effective way to analyze multimodal data sets. the findings highlight the potential of the acmtf method with nmi-based similarity criterion for uncovering hidden patterns in eeg and fmri data.