Cortical Reorganization Characterization Using Multimodal EEG − FNIRS Integration Analysis
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Introduction: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG suffers from poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that, both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal EEG-fNIRS integration analysis approach. The main goal of this dissertation is to develop and implement multimodal EEG/fNIRS integration analysis method for the characterization of cortical reorganization. Methods: The first part of the dissertation primary focuses on the experimental validation of the inherent correlation between neuronal activity and hemodynamic response from views of fNIRS-guided and EEG-guided, respectively. After that, we develop a novel fNIRS-informed EEG source imaging approach, by fusing the high spatial resolution of fNIRS and high temporal resolution of EEG, to investigate the cortical activity with good spatiotemporal resolution. Leveraging the high spatiotemporal resolution cortical activity, detailed cortical network alterations, can be subsequently estimated, providing a complete cortical-level characterization of the brain activity. Results: Through an fNIRS-guided hybrid EEG-fNIRS brain computer interface (BCI) study and an EEG-guided fNIRS analysis study, we validate that the complementary information offered by EEG and fNIRS is beneficial to the investigation of cortical activity. In addition, the novel fNIRS-informed EEG source imaging, is developed, validated and applied in studying the brain network alterations induced by Alzheimer’s disease and stroke. Conclusion: The novel fNIRS/EEG integration methods and subsequent brain network analysis presented in this dissertation have provided the tools and technologies for cortical-level assessment of normal brain activity and characterizing cortical reorganization associated with brain disorders.