Hierarchical latent dictionaries for models of brain activation

Journal of Machine Learning Research

Volumn 22, Issue , 2012, Pages 409-421

  Fyshe, Alona ^a   Fox, Emily ^b   Dunson, David ^c   Mitchell, Tom ^a  

^a Carnegie Mellon University   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BRAIN MAPPING; KNOWLEDGE MANAGEMENT; MAGNETOENCEPHALOGRAPHY;

BRAIN ACTIVATION; BRAIN ACTIVITY; HUMAN SUBJECTS; SAMPLE SIZES; SENSOR MEASUREMENTS; TIME-VARYING MEANS;

BRAIN;

EID: 84954223375     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (30)

1. S. Behseta, Robert E. Kass, and Garrick L. Wall-Strom. Hierarchical models for assessing variability among functions. Biometrika, 92(2):419-434, June 2005. ISSN 0006-3444.
2. A. Bhattacharya and D.B. Dunson. Sparse Bayesian infinite factor models. Biometrika, 98(2):291-306, 2011.
3. S.P. Brooks and A. Gelman. General methods for general methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7:434-455, 1998.
4. Carlos M Carvalho, Jeffrey Chang, Joseph E Lucas, Joseph R Nevins, Quanli Wang, and Mike West. High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics. Journal of the American Statistical Association, 103(484):1438-1456, December 2008. ISSN 0162-1459.
5. Stavros I Dimitriadis, Nikolaos A Laskaris, Vasso Tsirka, Michael Vourkas, Sifis Micheloyannis, and Spiros Fotopoulos. Tracking brain dynamics via time-dependent network analysis. Journal of Neuroscience Methods, 193(1):145-155, 2010.
6. Guido Dornhege, Benjamin Blankertz, Gabriel Curio, and Klaus-Robert Miiller. Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE transactions on bio-medical engineering, 51(6):993-1002, June 2004. ISSN 0018-9294.
7. Emily Fox and David Dunson. Bayesian Nonparamet-ric Covariance Regression, 2011. URL http://arxiv. org/abs/1101.2017v2.
8. Roman Freunberger, Markus Werkle-Bergner, Bir-git Griesmayr, Ulman Lindenberger, and Wolfgang Klimesch. Brain Oscillatory Correlates of Working Memory Constraints. Brain research, 1375:93-102, December 2010. ISSN 1872-6240.
9. Alan E. Gelfand, Alexandra M. Schmidt, Sudipto Banerjee, and C. F. Sirmans. Nonstationary mul-tivariate process modeling through spatially varying coregionalization. Test, 13(2):263-312, December 2004. ISSN 1133-0686. doi: 10.1007/BF02595775.
10. Andrew Gelman and Donald B Rubin. Inference from iterative simulation using multiple sequences. Statistical Science, 7(4):457-472, 1992.
11. Joyee Ghosh and David B Dunson. Default Prior Distributions and Efficient Posterior Computation in Bayesian Factor Analysis. Journal of Computational and Graphical Statistics, 18(2):306-320, 2009.
12. Adrian R Groves, Michael A Chappell, and Mark W Woolrich. Combined spatial and non-spatial prior for inference on MRI time-series. NeuroImage, 45(3):795-809, 2009.
13. Marcos Perreau Guimaraes, Dik Kin Wong, E Timothy Uy, Logan Grosenick, and Patrick Suppes. Single-trial classification of MEG recordings. IEEE transactions on bio-medical engineering, 54(3):436-43, March 2007. ISSN 0018-9294.
14. M. Hamalainen, R. Hari, R.J. Ilmoniemi, J. Knuutila, and O.V. Lounasmaa. Magnetoencephalographythe-ory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews ofmod-ern Physics, 65(2), 1993.
15. Peter Hansen, Morten Kringelbach, and Riitta Salmelin. MEG: An Introduction to Methods. Oxford University Press, USA, 2010. ISBN 0195307232.
16. Stefan J Kiebel, Jean Daunizeau, Christophe Phillips, and Karl J Friston. Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG. NeuroImage, 39(2):728-41, January 2008. ISSN 10538119.
17. David Knowles and Zoubin Ghahramani. Infinite Sparse Factor Analysis and Infinite Independent Components Analysis. In International Conference on Independent Component Analysis and Signal Separation, pages 381-388, 2007.
18. Steven Lemm, Benjamin Blankertz, Gabriel Curio, and Klaus-Robert Muller. Spatio-spectral filters for improving the classification of single trial EEG. IEEE transactions on bio-medical engineering, 52(9):1541-8, September 2005. ISSN 0018-9294.
19. Hedibert Freitas Lopes, Esther Salazar, and Dani Gamerman. Spatial dynamic factor analysis. Bayesian Analysis, 3(4):759-792, 2008. ISSN 19316690.
20. Mark Palatucci, Geoffrey Hinton, Dean Pomerleau, and Tom M Mitchell. Zero-Shot Learning with Semantic Output Codes. Advances in Neural Information Processing Systems, 22:1410-1418, 2009.
21. C. E. Rasmussen and C. K. I. Williams. Gaussian processes for machine learning, volume 14. The MIT Press, April 2006.
22. S Taulu and J Simola. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine and Biology, 51:1-10, 2006.
23. Samu Taulu and Riitta Hari. Removal of magnetoen-cephalographic artifacts with temporal signal-space separation: demonstration with single-trial auditory-evoked responses. Human brain mapping, 30(5):1524-34, May 2009. ISSN 1097-0193.
24. Samu Taulu, Matti Kajola, and Juha Simola. The Signal Space Separation method. ArXiv Physics, 2004. URL http://arxiv.org/abs/physics/0401166.
25. Y.W. Teh, Matthias Seeger, and M.I. Jordan. Semi-parametric latent factor models. In R. G. Cowell and Z. Ghahramani, editors, Workshop on Artificial Intelligence and Statistics, volume 10, pages 333-340. Citeseer, 2005.
26. Nelson J Trujillo-Barreto, Eduardo Aubert-Vazquez, and Pedro a Valdes-Sosa. Bayesian model averaging in EEG/MEG imaging. NeuroImage, 21(4):1300-19, April 2004. ISSN 1053-8119.
27. M A Uusitalo and R J Ilmoniemi. Signal-space projection method for separating MEG or EEG into components. Medical & biological engineering & computing, 35(2):135-40, March 1997. ISSN 0140-0118.
28. Mike West. Bayesian Factor Regression Models in the Large p, Small n Paradigm. Bayesian Statistics, 7 (2003):723-732, 2003.
29. Andrew Wilson and Zoubin Ghahramani. Generalised wishart processes. In The 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain, July 2011.
30. David P Wipf, Julia P Owen, Hagai T Attias, Ken-Suke Sekihara, and Srikantan S Nagarajan. Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG. NeuroImage, 49(1):641-55, January 2010. ISSN 1095-9572.