I am a postdoctoral researcher working with Scott Makeig on advanced signal processing algorithms for the analysis of EEG data.

Current Interests

• Understanding and characterizing the alpha and theta rhythms using statistical signal processing, dynamical systems, and computational neuroscience methods.
• General information theoretic properties of independence and mutual information based transformations for large sensor arrays,
  with application to EEG and speech, image and video coding.
• Development of the AMICA program for large scale adaptive parallel ICA.
• Development of VLSI implentation of convolutive ICA with adaptive linear process mixture model sources.

AMICA

Matlab code:

• amica.m -- matlab code to perform AMICA adaptive ICA mixture model algorithm
• amica_demo.m -- a demo matlab program for the AMICA algorithm, includes code for generation of multiple linear mixtures of Generalized Gaussian random variables

Publications:

Conferences:

• J. A. Palmer and  S. Makeig, Blind Separation of Dependent Sources and Subspaces by Minimum Mutual Information, submitted to ICASSP 2010.
• J. A. Palmer, K. Kreutz-Delgado, and  S. Makeig, Probabilistic Formulation of Independent Vector Analysis using Complex Gaussian Scale Mixtures, accepted to ICA 2009.
• J. A. Palmer, S. Makeig, and  K. Kreutz-Delgado, A Complex Cross-spectral Distribution Model using Normal Variance Mean Mixtures, accepted to ICASSP 2009.
• J. A. Palmer, K. Kreutz-Delgado, and S. Makeig, Sparse Independent Vector Analysis: Dictionary Design using a fast ICA/IVA Mixture Model, 2008 SIAM Conference on Imaging Science, San Diego, CA, July 7, 2008.
• J. A. Palmer, S. Makeig, K. Kreutz-Delgado, and B. D. Rao, Newton Method for the ICA Mixture Model, in Proceedings of the 33rd IEEE International Conference on Acoustics and Signal Processing (ICASSP 2008), Las Vegas, NV, pp. 1805-1808, 2008. (talk slides)
• J. A. Palmer, K. Kreutz-Delgado, B. D. Rao, and S. Makeig, Modeling and Estimation of Dependent Subspaces with Non-Radially Symmetric and Skewed Densities, Proceedings of the 7th International Symposium on Independent Component Analysis, Edited by Mike E. Davies, Christopher J. James, Samer A. Abdallah and Mark D Plumbley, Lecture Notes in Computer Science, Springer, 2007. (talk slides)
• J. A. Palmer, K. Kreutz-Delgado, and S. Makeig, Super-Gaussian Mixture Source Model for ICA, Proceedings of the 6th International Symposium on Independent Component Analysis, Edited by Justinian Rosca, Deniz Erdogmus, Jose C. Principe and Simon Haykin, Lecture Notes in Computer Science, Springer, 2006.
• J. A. Palmer, K. Kreutz-Delgado, D. P. Wipf, and B. D. Rao, Variational EM Algorithms for Non-Gaussian Latent Variable Models, Advances in Neural Information Processing Systems 18, Proceedings of the 2005 Conference, Edited by Yair Weiss, Bernhard Scholkopf and John Platt, MIT Press, 2006.
• J. A. Palmer and K. Kreutz-Delgado, A General Framework for Component Estimation, Proceedings of the 4th International Symposium on Independent Component Analysis, 2003.
• J. A. Palmer and K. Kreutz-Delgado, A Globally Convergent Algorithm for MAP Estimation with Non-Gaussian Priors, Proceedings of the 36th Asilomar Conference on Signals and Systems, 2002.

Papers in preparation:

• J. A. Palmer, K. Kreutz-Delgado, and S. Makeig,  Asymptotic Newton Method for the ICA Mixture Model with Adaptive Source Densities (in preparation).
• J. A. Palmer, K. Kreutz-Delgado, and S. Makeig,  Dependency Models based on Generalized Gaussian Scale Mixtures and Normal Variance Mean Mixtures (in preparation).
  References:
          - O. Barndorff-Nielsen, J. Kent, and M. Sorensen., Normal Variance-Mean Mixtures and z Distributions.
          - T. Gneiting, Normal Scale Mixtures and Dual Probability Densities.
• J. A. Palmer, Solution of Differential Equations of Hypergeometric Type, (in preparation).
• J. A. Palmer, Differential Criteria for Positive Definiteness, (in preparation).

Technical Reports

• J. A. Palmer, K. Kreutz-Delgado, and S. Makeig, Mixture Convolutive Independent Component Analysis, Technical Report, UCSD, 2006.
• J. A. Palmer, K. Kreutz-Delgado, and S. Makeig, An Independent Component Analysis Mixture Model with Adaptive Source Densities, Technical Report, UCSD, 2006.
• J. A. Palmer and K. Kreutz-Delgado, Generalized Kernel Classification and Regression. Technical Report, UCSD, 2004.
• J. A. Palmer, Relatve Convexity, Technical Report, UCSD, 2003.

Dissertation:

• J. A. Palmer, Variational and Scale Mixture Representations of Non-Gaussian Densities for Estimation in the Bayesian Linear Model: Sparse Coding, Independent Component Analysis, and Minimum Entropy Segmentation, Ph.D. Thesis, University of California San Diego, 2006.
• Defense Slides

Synopticon