In this paper, we discuss the application of the mixtures of Gaussians model for density estimation to the analysis of fMRI time series.
Abstract. In this paper, we discuss the application of the mixtures of Gaussians model for density estimation to the analysis of fMRI time series.
In this paper, we discuss the application of the mixtures of Gaussians model for density estimation to the analysis of fMRI time series.
Here, we propose an improved method which is based on Gaussian Mixture Models (GMM) and shows greater robustness for noise compared to traditional ALFF. Our ...
We describe in this work an exploratory analysis of fMRI data. We regard the fMRI dataset as a set of spatiotemporal signals S<sub>x</sub> indexed by their ...
Abstract—In this paper, a new Markov random field-based mixture model, where each of its components is a mixture of. Student's-t and Rayleigh distributions, ...
We compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM.
GMMs are employed in this work to extract the activation patterns from functional Magnetic Resonance Imaging (fMRI) data. The highly correlated time-series ...
GMMs are employed in this work to extract the activation patterns from functional Magnetic Resonance Imaging (fMRI) data. The highly correlated time-series ...
This chapter discusses issues specific to the analysis of fMRI data. It extends the Generalized. Linear Model (GLM) introduced in Chapter 7 to linear ...