Advances in Neuroimaging Data Processing

ISBN 10: 3036569987 / ISBN 13: 9783036569987

Published by MDPI AG, 2023

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - The development of in vivo neuroimaging techniques has yielded an incredible amount of digital information about the brain. Neuroimaging techniques are increasingly being used to study human cognitive processes, create brain-machine interfaces, and also to identify and diagnose certain brain disorders. Currently, neuroscientists and medics actively use different methods for brain scans, including electro- and magnetoencephalography (EEG/MEG), functional near-infrared spectroscopy (fNIRS), electrocorticography (ECoG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI). Recent advances in signal processing and machine learning for neuroimaging data using various signal processing methods have made impressive progress in solving a number of practical tasks in medicine, healthcare, neuroscience, biomedical engineering, brain-machine interfaces, and cognitive science, to name but a few. This Special Issue aims to provide a forum for academic and industrial communities to present and discuss the latest theoretical and experimental results related to recent advances in neuroimaging data processing in terms of new theories, algorithms, architectures, and applications.

Seller Inventory # 9783036569987

Bibliographic Details

• Title: Advances in Neuroimaging Data Processing
• Publisher: MDPI AG
• Publication Date: 2023
• Language: English
• ISBN 10: 3036569987
• ISBN 13: 9783036569987
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 17D millimeters 250 millimeters width by 175 millimeters height by 17 millimeters depth
• Weight: 665 grams

About this title

Synopsis

The development of in vivo neuroimaging techniques has yielded an incredible amount of digital information about the brain. Neuroimaging techniques are increasingly being used to study human cognitive processes, create brain-machine interfaces, and also to identify and diagnose certain brain disorders. Currently, neuroscientists and medics actively use different methods for brain scans, including electro- and magnetoencephalography (EEG/MEG), functional near-infrared spectroscopy (fNIRS), electrocorticography (ECoG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI). Recent advances in signal processing and machine learning for neuroimaging data using various signal processing methods have made impressive progress in solving a number of practical tasks in medicine, healthcare, neuroscience, biomedical engineering, brain-machine interfaces, and cognitive science, to name but a few. This Special Issue aims to provide a forum for academic and industrial communities to present and discuss the latest theoretical and experimental results related to recent advances in neuroimaging data processing in terms of new theories, algorithms, architectures, and applications.