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A Machine Learning Model to Predict Seizure Susceptibility from Resting-State fMRI Connectivity.

, , , , , and . SpringSim, page 1-11. IEEE, (2019)

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Noninvasive Intracranial Pressure Assessment Based on a Data-Mining Approach Using a Nonlinear Mapping Function., , , , , and . IEEE Trans. Biomed. Eng., 59 (3): 619-626 (2012)High-resolution electroencephalographic forward modeling in traumatic brain injury using the finite element method., , , , , and . ISBI, page 990-993. IEEE, (2013)A Machine Learning Model to Predict Seizure Susceptibility from Resting-State fMRI Connectivity., , , , , and . SpringSim, page 1-11. IEEE, (2019)A semi-automated workflow solution for multimodal neuroimaging: application to patients with traumatic brain injury., , , , , , , and . Brain Informatics, 3 (1): 1-15 (2016)Automatic Detection of EEG Epileptiform Abnormalities in Traumatic Brain Injury using Deep Learning., , , , , , and . EMBC, page 302-305. IEEE, (2021)Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients., , , , , , , , , and . Frontiers Neuroinformatics, (2018)Lesion Normalization and Supervised Learning in Post-traumatic Seizure Classification with Diffusion MRI., , , , , , , , and . CDMRI@MICCAI, volume 13006 of Lecture Notes in Computer Science, page 133-143. Springer, (2021)Integration of Multimodal Neuroimaging and Electroencephalography for the Study of Acute Epileptiform Activity After Traumatic Brain Injury., , , and . DILS, volume 9162 of Lecture Notes in Computer Science, page 165-179. Springer, (2015)Estimation of Hidden State Variables of the Intracranial System Using Constrained Nonlinear Kalman Filters., , , , , and . IEEE Trans. Biomed. Eng., 54 (4): 597-610 (2007)Forecasting ICP Elevation Based on Prescient Changes of Intracranial Pressure Waveform Morphology., , , , and . IEEE Trans. Biomed. Eng., 57 (5): 1070-1078 (2010)