Inferring Brain Dynamics via Multimodal Joint Graph Representation EEG-fMRI
This provides a new perspective for diagnosing functional changes in neuroplasticity studies, though it appears incremental as it builds on existing graph-based and deep learning methods.
The paper tackles the problem of analyzing simultaneously acquired EEG-fMRI data to understand brain dynamics, using multimodal joint graph representation to extract effective brain components across temporal and spatial dimensions.
Recent studies have shown that multi-modeling methods can provide new insights into the analysis of brain components that are not possible when each modality is acquired separately. The joint representations of different modalities is a robust model to analyze simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI). Advances in precision instruments have given us the ability to observe the spatiotemporal neural dynamics of the human brain through non-invasive neuroimaging techniques such as EEG & fMRI. Nonlinear fusion methods of streams can extract effective brain components in different dimensions of temporal and spatial. Graph-based analyzes, which have many similarities to brain structure, can overcome the complexities of brain mapping analysis. Throughout, we outline the correlations of several different media in time shifts from one source with graph-based and deep learning methods. Determining overlaps can provide a new perspective for diagnosing functional changes in neuroplasticity studies.