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dc.contributor.editorHramov, Alexander E.
dc.contributor.editorPisarchik, Alexander N.
dc.date.accessioned2023-04-05T13:00:41Z
dc.date.available2023-04-05T13:00:41Z
dc.date.issued2023
dc.identifierONIX_20230405_9783036569987_231
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/98952
dc.description.abstractThe 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::M Medicine and Nursingen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciencesen_US
dc.subject.otherMEG
dc.subject.otherFieldTrip
dc.subject.otherBrainstorm
dc.subject.othersource reconstruction
dc.subject.otherflickering
dc.subject.othercognitive neuroscience
dc.subject.othervisual perception
dc.subject.otherdetrended fluctuation analysis
dc.subject.otherlong-range correlations
dc.subject.otherelectroencephalogram
dc.subject.othersleep deprivation
dc.subject.othernonstationarity
dc.subject.othertransfer entropy
dc.subject.otherkernel methods
dc.subject.otherRenyi’s entropy
dc.subject.otherconnectivity analysis
dc.subject.otherphase interactions
dc.subject.otherbrain data
dc.subject.otherlow-cost devices
dc.subject.otherEEG
dc.subject.otherBIDS
dc.subject.otherneuroscience
dc.subject.otherlibrary
dc.subject.otherambiguous stimuli
dc.subject.otherNecker cubes
dc.subject.otherclassification task
dc.subject.otherEEG analysis
dc.subject.otherwavelet analysis
dc.subject.otherdecision time
dc.subject.otherperceptual errors
dc.subject.othertime-on-task effect
dc.subject.otherbrain connectivity
dc.subject.otherartificial neural networks
dc.subject.othergranger causality
dc.subject.othertime series
dc.subject.otherbrain–computer interface
dc.subject.otherevent-related potential
dc.subject.otherbeamforming
dc.subject.otherregularization
dc.subject.otherBCI
dc.subject.othergraphical user interface
dc.subject.otherwheelchair navigation
dc.subject.othergrid map
dc.subject.othernatural landmark
dc.subject.otheroptimal paths
dc.subject.otherdeep Q-networks
dc.subject.otherfocal epilepsy
dc.subject.otherdiffusion imaging
dc.subject.otherelectroencephalography
dc.subject.otherstructure-function coupling
dc.subject.otherseizure onset
dc.subject.otherstructural connectivity
dc.subject.otherfunctional connectivity
dc.subject.othercognitive load
dc.subject.othercoupling
dc.subject.otherbubble entropy
dc.subject.othertransition network
dc.subject.othern/a
dc.titleAdvances in Neuroimaging Data Processing
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-6999-4
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036569987
oapen.relation.isbn9783036569994
oapen.pages186
oapen.place.publicationBasel


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