In order to get around the inherent constraints of distinct imaging modalities, multimodal fusion in neuroimaging integrates data from various imaging modalities. Higher temporal and spatial precision, improved contrast, the correction of imaging distortions, and the bridging of physiological and cognitive data can all be achieved through neuroimaging fusion. This analysis aims to examine the fusion and optimization of the multimodal neuroimaging technique and to examine a multimodal neuroimaging-based technique for measuring brain fatigue. Four-dimensional consistency of local neural activities (FOCA) and local multimodal serial analysis (LMSA) are primarily presented to naturally merge electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI). With the time precision relying on EEG and the space precision focused on fMRI, the time–space matching in the data fusion system has acceptable outcomes, with the time precision above 88% and the space precision above 89%.
Authors: Pedada Sujata, Dattatray G. Takale, Swati Tyagi, Saniya Bhalerao, Mohit Tiwari, Joshuva Arockia Dhanraj
DOI: https://doi.org/10.1002/9781394197705.ch12
Publish Year: 2024
