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The brain is a complex system with functional and structural networks. Different neuroimaging methods have their strengths and limitations, depending on the signals they measure. Combining techniques like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques has gained interest, but understanding how the information derived from these modalities is related remains an exciting open question. Successful integration of these modalities requires a sophisticated mathematical framework that goes beyond simple comparative analyses. The multilayer network model has emerged as a promising approach. This study is an extended version of the conference paper “Resting State Brain Connectivity Analysis from EEG and FNIRS Signals” . In this study, we explored the brain network properties obtained from EEG and fNIRS data using graph analysis. Additionally, we adopted the multilayer network model to evaluate the benefits of combining multiple modalities compared to using a single modality. A small-world network structure was observed in the rest, right motor imagery, and left motor imagery tasks in both modalities. We found that EEG captures faster changes in neural activity, thus providing a more precise estimation of the timing of information transfer between brain regions in RS. fNIRS provides insights into the slower hemodynamic responses associated with longer-lasting and sustained neural processes in cognitive tasks. The multilayer approach outperformed unimodal analyses, offering a richer understanding of brain function. Complementarity between EEG and fNIRS was observed, particularly during tasks, as well as a certain level of redundancy and complementarity between the multimodal and the unimodal approach, which is dependent on the modality and on the specific brain state. Overall, the results highlight differences in how EEG and fNIRS capture brain network topology in RS and tasks and emphasize the value of integrating multiple modalities for a comprehensive view of brain connectivity and function.