Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Ricardo Bruña Fernandez"'
Autor:
Su Yang, Jose Miguel Sanchez Bornot, Ricardo Bruña Fernandez, Farzin Deravi, KongFatt Wong-Lin, Girijesh Prasad
Publikováno v:
Brain Informatics, Vol 8, Iss 1, Pp 1-11 (2021)
Abstract Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the Alzheimer’s disease (AD), one of the most common forms of dementia. However, most of the previous studies are limited to binary classificatio
Externí odkaz:
https://doaj.org/article/029afbddb6464c41975f8232c3136b33
Autor:
Su Yang, Jose Miguel Sanchez Bornot, Ricardo Bruña Fernandez, Farzin Deravi, Sanaul Hoque, KongFatt Wong-Lin, Girijesh Prasad
Publikováno v:
Sensors, Vol 21, Iss 18, p 6210 (2021)
Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence
Externí odkaz:
https://doaj.org/article/92c7a159030d45adb21c64d5422120e3
Autor:
Girijesh Prasad, Ricardo Bruña Fernandez, Jose Miguel Sanchez Bornot, Farzin Deravi, KongFatt Wong-Lin, Su Yang
Publikováno v:
Brain Informatics
Brain Informatics, Vol 8, Iss 1, Pp 1-11 (2021)
Brain Informatics, Vol 8, Iss 1, Pp 1-11 (2021)
Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the Alzheimer’s disease (AD), one of the most common forms of dementia. However, most of the previous studies are limited to binary classification and do
Autor:
Sanaul Hoque, Girijesh Prasad, Jose Miguel Sanchez Bornot, Ricardo Bruña Fernandez, Farzin Deravi, KongFatt Wong-Lin, Su Yang
Publikováno v:
Sensors (Basel, Switzerland)
Sensors
Sensors, Vol 21, Iss 6210, p 6210 (2021)
Volume 21
Issue 18
Sensors
Sensors, Vol 21, Iss 6210, p 6210 (2021)
Volume 21
Issue 18
Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence