In this work, the architectures of three partially adaptive STAP algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in construction of brain activation maps in fMRI. Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing time and maximum memory requirements, and thus demonstrates potential in fMRI analysis
The univariate approach without a smoothing filter for detecting activation patterns in functional m...
Data from functional magnetic resonance imaging (fMRI) consist of time series of brain images that a...
Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging non-invasive method that uses magneti...
This paper introduces three partially adaptive space-time processing (STAP) schemes for analyzing fM...
This paper introduces three partially adaptive space-time processing (STAP) schemes for analyzing fM...
In this work, the architectures of three partially adaptive STAP algorithms are introduced, one of w...
Partially adaptive STAP for fMRI: a method for detecting brain activation regions in simulation and ...
Space-time adaptive processing (STAP), previously developed in the field of sensor array processing ...
Space-time adaptive processing (STAP), previously developed in the field of sensor array processing ...
Since its development in the early 1990s, functional MRI has emerged as a useful tool to explore the...
Based on the space-time adaptive processing (STAP) model we developed, an improved STAP model is int...
Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, wh...
Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. With...
Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, wh...
Abstract—This significantly extends Multi-Voxel Pattern Analysis (MVPA) methods, such as the Searchl...
The univariate approach without a smoothing filter for detecting activation patterns in functional m...
Data from functional magnetic resonance imaging (fMRI) consist of time series of brain images that a...
Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging non-invasive method that uses magneti...
This paper introduces three partially adaptive space-time processing (STAP) schemes for analyzing fM...
This paper introduces three partially adaptive space-time processing (STAP) schemes for analyzing fM...
In this work, the architectures of three partially adaptive STAP algorithms are introduced, one of w...
Partially adaptive STAP for fMRI: a method for detecting brain activation regions in simulation and ...
Space-time adaptive processing (STAP), previously developed in the field of sensor array processing ...
Space-time adaptive processing (STAP), previously developed in the field of sensor array processing ...
Since its development in the early 1990s, functional MRI has emerged as a useful tool to explore the...
Based on the space-time adaptive processing (STAP) model we developed, an improved STAP model is int...
Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, wh...
Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. With...
Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, wh...
Abstract—This significantly extends Multi-Voxel Pattern Analysis (MVPA) methods, such as the Searchl...
The univariate approach without a smoothing filter for detecting activation patterns in functional m...
Data from functional magnetic resonance imaging (fMRI) consist of time series of brain images that a...
Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging non-invasive method that uses magneti...