Subject-Specific Modeling of EEG-fNIRS Neurovascular Coupling by Task-Related Tensor Decomposition

In this study, we proposed a novel framework to enhance the subject-specific parametric modeling of NVC from simultaneous EEG-fNIRS measurement. Specifically, task-related tensor decomposition of high-order EEG data was performed to extract the underlying connections in the temporal-spectral-spatial structures of EEG activities and identify the most relevant temporal signature within multiple trials. Subject-specific HRFs were estimated by parameters optimization of a double gamma function model. A canonical motor task experiment was designed to induce neural activity and validate the effectiveness of the proposed framework. The results indicated that the proposed framework significantly improves the reproducibility of EEG components and the correlation between the predicted hemodynamic activities and the real fNIRS signal. Moreover, the estimated parameters characterized the NVC differences in the task with two speeds. Therefore, the proposed framework provides a feasible solution for the quantitative assessment of the NVC function.
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - Category: Neuroscience Source Type: research