Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Deniz Kocanaogullari"'
Autor:
Jennifer Mak, Deniz Kocanaogullari, Xiaofei Huang, Jessica Kersey, Minmei Shih, Emily S. Grattan, Elizabeth R. Skidmore, George F. Wittenberg, Sarah Ostadabbas, Murat Akcakaya
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 30, Pp 1840-1850 (2022)
We aim to build a system incorporating electroencephalography (EEG) and augmented reality (AR) that is capable of identifying the presence of visual spatial neglect (SN) and mapping the estimated neglected visual field. An EEG-based brain-computer in
Externí odkaz:
https://doaj.org/article/5d41b814662843ffbf7e5c8533b2c6b1
Publikováno v:
SLEEP. 46:A335-A336
Introduction Children with sensory hypersensitivities have poorer subjective sleep health than their peers. However, traditional actigraphy variables (e.g., sleep efficiency, sleep duration) do not adequately capture these sleep deficits. In qualitat
Autor:
Deniz Kocanaogullari, Xiaofei Huang, Jennifer Mak, Minmei Shih, Elizabeth Skidmore, George F. Wittenberg, Sarah Ostadabbas, Murat Akcakaya
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2021
Spatial neglect (SN) is a neurological disorder that causes inattention to visual stimuli in the contralesional visual field, stemming from unilateral brain injury such as stroke. The current gold standard method of SN assessment, the conventional Be
Autor:
Aya Khalaf, Sarah Ostadabbas, Jennifer Mak, George F. Wittenberg, Elizabeth R. Skidmore, Jessica Kersey, Murat Akcakaya, Deniz Kocanaogullari
Publikováno v:
EMBC
Spatial neglect (SN) is a neurological syndrome in stroke patients, commonly due to unilateral brain injury. It results in inattention to stimuli in the contralesional visual field. The current gold standard for SN assessment is the behavioral inatte
Publikováno v:
MLSP
After their triumph in various classification, recognition and segmentation problems, deep learning and convolutional networks are now making great strides in different inverse problems of imaging. Magnetic resonance image (MRI) reconstruction is an