Optimizing ICA in fMRI using information on spatial regularities of the sources.

[1]  Christopher J. James,et al.  Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis , 2003, IEEE Transactions on Biomedical Engineering.

[2]  Rainer Goebel,et al.  Real-time independent component analysis of fMRI time-series , 2003, NeuroImage.

[3]  Rainer Goebel,et al.  Dynamic Premotor-to-Parietal Interactions during Spatial Imagery , 2008, The Journal of Neuroscience.

[4]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[5]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[6]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[7]  S. Ruan,et al.  A multistep Unsupervised Fuzzy Clustering Analysis of fMRI time series , 2000, Human brain mapping.

[8]  T. Adali,et al.  Ieee Workshop on Machine Learning for Signal Processing Semi-blind Ica of Fmri: a Method for Utilizing Hypothesis-derived Time Courses in a Spatial Ica Analysis , 2022 .

[9]  Thomas Dierks,et al.  Tracking the Mind's Image in the Brain II Transcranial Magnetic Stimulation Reveals Parietal Asymmetry in Visuospatial Imagery , 2002, Neuron.

[10]  Tao Wang,et al.  Simulated Annealing with Asymptotic Convergence for Nonlinear Constrained Global Optimization , 1999, CP.

[11]  R. Goebel,et al.  Tracking the Mind's Image in the Brain I Time-Resolved fMRI during Visuospatial Mental Imagery , 2002, Neuron.

[12]  Mikhail J. Atallah,et al.  Algorithms and Theory of Computation Handbook , 2009, Chapman & Hall/CRC Applied Algorithms and Data Structures series.

[13]  Rainer Goebel,et al.  Spatial independent component analysis of functional MRI time‐series: To what extent do results depend on the algorithm used? , 2002, Human brain mapping.

[14]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[15]  D B Rowe,et al.  Bayesian source separation for reference function determination in fMRI , 2001, Magnetic resonance in medicine.

[16]  Allan Kardec Barros,et al.  Extraction of Specific Signals with Temporal Structure , 2001, Neural Computation.

[17]  E. Formisano,et al.  Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest , 2004, Human brain mapping.

[18]  Daniel B. Rowe,et al.  Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing , 2002 .

[19]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[20]  John C. Gore,et al.  ROC Analysis of Statistical Methods Used in Functional MRI: Individual Subjects , 1999, NeuroImage.

[21]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[22]  T. Sejnowski,et al.  Human Brain Mapping 6:368–372(1998) � Independent Component Analysis of fMRI Data: Examining the Assumptions , 2022 .

[23]  D. Heeger,et al.  Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1 , 1996, The Journal of Neuroscience.

[24]  Rainer Goebel,et al.  Cortex-based independent component analysis of fMRI time series. , 2004 .

[25]  Vince D. Calhoun,et al.  A Feature-Selective Independent Component Analysis Method for Functional MRI , 2007, Int. J. Biomed. Imaging.

[26]  Bo Hu,et al.  Principal independent component analysis , 1999, IEEE Trans. Neural Networks.

[27]  Kevin H. Knuth.  A Bayesian approach to source separation , 1999 .

[28]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[29]  Wei Lu,et al.  Approach and applications of constrained ICA , 2005, IEEE Transactions on Neural Networks.

[30]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[31]  R Baumgartner,et al.  Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis. , 2000, Magnetic resonance imaging.

[32]  Ramón Miralles,et al.  Independent component analysis with prior information about the mixing matrix , 2003, Neurocomputing.

[33]  Rainer Goebel,et al.  Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis. , 2007, Magnetic resonance imaging.

[34]  D. Rowe.  A Bayesian approach to blind source separation , 2002 .

[35]  Tzyy-Ping Jung,et al.  Imaging brain dynamics using independent component analysis , 2001, Proc. IEEE.