I uploaded the workshop paper by Prof. B.D.Rao and Prof. K.Kreutz-Delgado, which was presented in the 8th IEEE Digital Signal Processing Workshop, Bryce Canyon, UT, 1998. The workshop paper can be downloaded here.
Probably this is the first paper on the multiple measurement vector (MMV) model. As you can see, the MMV versions of FOCUSS, Matching Pursuit, Order Recursive Matching Pursuit, and Modified Matching Pursuit were all presented in this paper. These contents were fully discussed and extended in their journal paper under the same title (Sparse solutions to linear inverse problems with multiple measurement vectors). But unfortunately, the journal paper was published seven years later!!!
Monday, July 25, 2011
Thursday, July 21, 2011
Academic Software Applications for Electromagnetic Brain Mapping Using MEG and EEG
The following is the content. You will see many famous softwares are discussed in this special issue.
Academic Software Applications for Electromagnetic Brain Mapping Using MEG and EEG, Sylvain Baillet, Karl Friston, and Robert Oostenveld
Volume 2011 (2011), Article ID 972050, 4 pages
Brainstorm: A User-Friendly Application for MEG/EEG Analysis, François Tadel, Sylvain Baillet, John C. Mosher, Dimitrios Pantazis, and Richard M. Leahy
Volume 2011 (2011), Article ID 879716, 13 pages
Spatiotemporal Analysis of Multichannel EEG: CARTOOL, Denis Brunet, Micah M. Murray, and Christoph M. Michel
Volume 2011 (2011), Article ID 813870, 15 pages
EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing, Arnaud Delorme, Tim Mullen, Christian Kothe, Zeynep Akalin Acar, Nima Bigdely-Shamlo, Andrey Vankov, and Scott Makeig
Volume 2011 (2011), Article ID 130714, 12 pages
ELAN: A Software Package for Analysis and Visualization of MEG, EEG, and LFP Signals, Pierre-Emmanuel Aguera, Karim Jerbi, Anne Caclin, and Olivier Bertrand
Volume 2011 (2011), Article ID 158970, 11 pages
ElectroMagnetoEncephalography Software: Overview and Integration with Other EEG/MEG Toolboxes, Peter Peyk, Andrea De Cesarei, and Markus Junghöfer
Volume 2011 (2011), Article ID 861705, 10 pages
FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data, Robert Oostenveld, Pascal Fries, Eric Maris, and Jan-Mathijs Schoffelen
Volume 2011 (2011), Article ID 156869, 9 pages
MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG, Sarang S. Dalal, Johanna M. Zumer, Adrian G. Guggisberg, Michael Trumpis, Daniel D. E. Wong, Kensuke Sekihara, and Srikantan S. Nagarajan
Volume 2011 (2011), Article ID 758973, 17 pages
EEG and MEG Data Analysis in SPM8, Vladimir Litvak, Jérémie Mattout, Stefan Kiebel, Christophe Phillips, Richard Henson, James Kilner, Gareth Barnes, Robert Oostenveld, Jean Daunizeau, Guillaume Flandin, Will Penny, and Karl Friston
Volume 2011 (2011), Article ID 852961, 32 pages
EEGIFT: Group Independent Component Analysis for Event-Related EEG Data, Tom Eichele, Srinivas Rachakonda, Brage Brakedal, Rune Eikeland, and Vince D. Calhoun
Volume 2011 (2011), Article ID 129365, 9 pages
LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data, Cyril R. Pernet, Nicolas Chauveau, Carl Gaspar, and Guillaume A. Rousselet
Volume 2011 (2011), Article ID 831409, 11 pages
Ragu: A Free Tool for the Analysis of EEG and MEG Event-Related Scalp Field Data Using Global Randomization Statistics, Thomas Koenig, Mara Kottlow, Maria Stein, and Lester Melie-García
Volume 2011 (2011), Article ID 938925, 14 pages
BioSig: The Free and Open Source Software Library for Biomedical Signal Processing, Carmen Vidaurre, Tilmann H. Sander, and Alois Schlögl
Volume 2011 (2011), Article ID 935364, 12 pages
Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research, Alan D. Degenhart, John W. Kelly, Robin C. Ashmore, Jennifer L. Collinger, Elizabeth C. Tyler-Kabara, Douglas J. Weber, and Wei Wang
Volume 2011 (2011), Article ID 363565, 13 pages
rtMEG: A Real-Time Software Interface for
Magnetoencephalography, Gustavo Sudre, Lauri Parkkonen, Elizabeth Bock, Sylvain Baillet, Wei Wang, and Douglas J. Weber
Volume 2011 (2011), Article ID 327953, 7 pages
BrainNetVis: An Open-Access Tool to Effectively Quantify and Visualize Brain Networks, Eleni G. Christodoulou, Vangelis Sakkalis, Vassilis Tsiaras, and Ioannis G. Tollis
Volume 2011 (2011), Article ID 747290, 12 pages
fMRI Artefact Rejection and Sleep Scoring Toolbox, Yves Leclercq, Jessica Schrouff, Quentin Noirhomme, Pierre Maquet, and Christophe Phillips
Volume 2011 (2011), Article ID 598206, 11 pages
Highly Automated Dipole EStimation (HADES), C. Campi, A. Pascarella, A. Sorrentino, and M. Piana
Volume 2011 (2011), Article ID 982185, 11 pages
Forward Field Computation with OpenMEEG, Alexandre Gramfort, Théodore Papadopoulo, Emmanuel Olivi, and Maureen Clerc
Volume 2011 (2011), Article ID 923703, 13 pages
PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction, Forrest Sheng Bao, Xin Liu, and Christina Zhang
Volume 2011 (2011), Article ID 406391, 7 pages
TopoToolbox: Using Sensor Topography to Calculate Psychologically Meaningful Measures from Event-Related EEG/MEG, Xing Tian, David Poeppel, and David E. Huber
Volume 2011 (2011), Article ID 674605, 8 pages
Thursday, July 7, 2011
When Bayes Meets Big Data
In the June Issue of The ISBA Bulletin, Michael Jordan wrote an article titled "The Era of Big Data". The article discussed the possibility and challenges to apply Bayesian techniques to Big Data (e.g. terabytes, petabytes, exabytes and zettabytes). Michael pointed out several advantages of Bayes over non-Bayes, which I quote here:
(1) Analyses of Big Data often have an exploratory flavor rather than a confirmatory flavor. Some of the concerns over family-wise error rates that bedevil classical approaches to exploratory data analysis are mitigated in the Bayesian framework.
(2) In the sciences, Big Data problems often arise in the context of “standard models,” which are often already formulated in probabilistic terms. That is, significant prior knowledge is often present and directly amenable to Bayesian inference.
(3) Consider a company wishing to offer personalized services to tens of millions of users. Large amounts of data will have been collected for some users, but for most users there will be little or no data. Such situations cry out for Bayesian hierarchical modeling.
(4) The growing field of Bayesian nonparametrics provides tools for dealing with situations in which phenomena continue to emerge as data are collected. For example, Bayesian nonparametrics not only provides probability models that yield power-law distributions, but it provides inferential machinery that incorporate these distributions.
Based on my experience on compressed sensing, I feel that Bayes provides a more flexible way to exploit structured sparsity. Such power gained from Bayes cannot be gained from non-Bayes methods. However, Bayes is computationally demanding. So, combining Bayes and non-Bayes is my research theme in compressed sensing. This is why I wrote the two papers:
Z.Zhang, B.D.Rao, Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors, ICASSP 2011
Z. Zhang, B.D.Rao, Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity, ICML 2011 Workshop on Structured Sparsity
Above Pictures: Nepenthes. jamban (growing in my patio)
This rare species was discovered in the island of Sumatra in Indonesian in 2005. The pitchers have a unique toilet shape, so the plant was affectionately called jamban, which means toilet in Indonesian.
(1) Analyses of Big Data often have an exploratory flavor rather than a confirmatory flavor. Some of the concerns over family-wise error rates that bedevil classical approaches to exploratory data analysis are mitigated in the Bayesian framework.
(2) In the sciences, Big Data problems often arise in the context of “standard models,” which are often already formulated in probabilistic terms. That is, significant prior knowledge is often present and directly amenable to Bayesian inference.
(3) Consider a company wishing to offer personalized services to tens of millions of users. Large amounts of data will have been collected for some users, but for most users there will be little or no data. Such situations cry out for Bayesian hierarchical modeling.
(4) The growing field of Bayesian nonparametrics provides tools for dealing with situations in which phenomena continue to emerge as data are collected. For example, Bayesian nonparametrics not only provides probability models that yield power-law distributions, but it provides inferential machinery that incorporate these distributions.
Based on my experience on compressed sensing, I feel that Bayes provides a more flexible way to exploit structured sparsity. Such power gained from Bayes cannot be gained from non-Bayes methods. However, Bayes is computationally demanding. So, combining Bayes and non-Bayes is my research theme in compressed sensing. This is why I wrote the two papers:
Z.Zhang, B.D.Rao, Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors, ICASSP 2011
Z. Zhang, B.D.Rao, Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity, ICML 2011 Workshop on Structured Sparsity
Above Pictures: Nepenthes. jamban (growing in my patio)
This rare species was discovered in the island of Sumatra in Indonesian in 2005. The pitchers have a unique toilet shape, so the plant was affectionately called jamban, which means toilet in Indonesian.
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