The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from funct...
Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter J. Ramad...
The perplexing effects of noise and high feature dimensionality greatly complicate functional magnetic resonance imaging (fMRI) classification. In this paper, we present a novel f...
Classification of brain images obtained through functional magnetic resonance imaging (fMRI) poses a serious challenge to pattern recognition and machine learning due to the extrem...
This review focuses on dynamic causal analysis of functional magnetic resonance (fMRI) data to infer brain connectivity from a time series analysis and dynamical systems perspecti...
Alard Roebroeck, Anil K. Seth, Pedro A. Valdes-Sos...
In this paper, a novel non-stationary model of functional Magnetic Resonance Imaging (fMRI) time series is proposed. It allows us to account for some putative habituation effect a...
Data-driven analysis methods, in particular independent component analysis (ICA) has proven quite useful for the analysis of functional magnetic imaging (fMRI) data. In addition, ...
Abstract. Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented...
Functional Magnetic Resonance Imaging (fMRI) provides dynamical access into the complex functioning of the human brain, detailing the hemodynamic activity of thousands of voxels d...
Abstract. A new information-theoretic approach is presented for analyzing fMRI data to calculate the brain activation map. The method is based on a formulation of the mutualinforma...
Andy Tsai, John W. Fisher III, Cindy Wible, Willia...
This manuscript proposes a retrieval system for fMRI brain images. Our goal is to find a similaritymetric to enable us to support queries for “similar tasks” for retrieval on...
Bing Bai, Paul B. Kantor, Ali Shokoufandeh, Debora...