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.

采用多模态分析方法的 EEG-fMRI 研究 最近在非侵入性神经影像学技术（如 EEG 和 fMRI）中得到了广泛应用，我们在此通过非线性融合方法、基于图像解析和深度学习等多个角度诠释该方法对人脑功能变化的研究所带来的新视角。