Abstract

DE LA CRUZ OLIVARES, Wildys; ORELLANA GARCIA, Arturo  and  FUENTES GONZALEZ, Jesús Enrique. Fusion of Multimodal Neuroscience Data Generated in EEG-fMRI Studies, a Thematic Analysis. RCIM [online]. 2022, vol.14, n.2  Epub Dec 01, 2022. ISSN 1684-1859.

Brain activity has multiple attributes, including electrical, metabolic, hemodynamic, and hormonal. Modern methods for studying brain functions such as PET (Positron Emission Tomography), fMRI (Functional Magnetic Resonance Imaging), and MEG (Magnetoencephalogram) are widely used by scientists. However, the EEG is a tool used for research and diagnosis due to its low cost, simplicity of use, mobility and the possibility of long-term monitoring of acquisition. To detect and interpret the relevant characteristics of these signals, each process is described by its temporal (EEG) and spatial (fMRI) scale. The present research focuses on conducting a bibliographic review on the integration of multimodal EEG-fMRI data that favors assessing its importance for the development of fusion algorithms and their use in the Cuban context. For this, documents with high rates of citations in the literature were analyzed, where precursor authors of the topics under analysis stand out. Multimodal EEG-fMRI studies generate multiple temporal and spatial data with high value for evidence-based medicine. Their integration provides added value in the search for new diagnostic methods, applying data mining, Deep learning and fusion algorithms. This work highlights the existence of low temporal resolution of fMRI and, on the other hand, the low spatial resolution of EEG, so the integration of both studies would increase the quality of their information.

Keywords : Analysis; EEG; fMRI; multimodal study; neuroscience data fusion.

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