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Human brain analysis and understanding pose several challenges due to the great complexity of the structural organization and the functional connectivity that characterizes the human brain. The ability of the brain to adapt in dynamic changes over time such as normal aging, neurodegenerative diseases or congenital brain malformations renders the brain’s exploration a particularly demanding and difficult task.In recent years, advances in brain imaging modalities and lately the multimodal fusion, combined with improvements in related technologies have greatly assisted the development of brain maps by providing insights regarding the overall brain structure and functionality. Even though the existence of sensory and motor maps for the human brain is known to some degree, the formation process is still subject to research.Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the two mostly used non-invasive brain imaging modalities that can track the changes in brain activity. Due to their complementary nature, high temporal resolution from EEG and high spatial resolution from fMRI, the fusion of simultaneous acquired EEG and fMRI recordings aims to provide complementary information about the brain functionality. In an effort to extend the current research in the field of brain understanding, a novel Brain Mapping Model (BMM) based on EEG and fMRI is proposed within this Ph.D. dissertation. The proposed BMM is based on the synergy of state-of-the-art computational techniques to associate the brain regional activities provided by the EEG-fMRI fusion. In more details, first, a novel formal model for the EEG signals’ representation is proposed. The proposed formal model enables the analysis and extraction of structural EEG features. The proposed method is based on the Syntactic Aggregate approXimation (SAX) algorithm, that in this work is improved by the Local-Global (LG) graph technique, to compose a Context Free-Grammar (CFG). Moreover, by modeling the EEG recordings with Stochastic Petri nets (SPNs) we are able to combine the EEG channels’ spatiotemporal dependencies. Second, two different EEG-fMRI fusion approaches are assessed in order to reveal the enhanced brain spatiotemporal resolution offered by the combination of the two modalities. The overarching goal of this BMM is to contribute to the further exploration and better understanding of the brain activities formulation with a future goal to be used in navigation applications for the visually impaired population.