Signal Processing for Brain State Monitoring and Brain-Computer Interfaces

Evolving Signal Processing for Brain-Computer Interfaces.
Makeig, S.; Kothe, C.; Mullen, T.; Bigdely-Shamlo, N.; Zhang, Z.; Kreutz-Delgado, K.  Proceedings of the IEEE. 2012 (invited survey, in press).

Strong Sub- and Super-Gaussianity.
Palmer, J.A.; Kreutz-Delgado, K.; Makeig, S. 9th International Symposium on Latent Variable Analysis Independent Component Analysis (LVA/ICA2010). Sept. 2010.

Probabilistic Formulation of Independent Vector Analysis using Complex Gaussian Scale Mixtures.
Palmer, J.A.; Kreutz-Delgado, K.; Makeig, S.   Proceedings of the 8th International Symposium on Independent Component Analysis (ICA2009). Edited by T. Adali, et al., Lecture Notes in Computer Science, Springer, 2009.

A Complex Cross-Spectral Distribution Model using Normal Variance Mean Mixtures.
Palmer, J.A.; Makeig, S.; Kreutz-Delgado, K.; Rao, B.D.  Proceedings of the 34th IEEE International Conference on Acoustics and Signal Processing (ICASSP 2009). May 2009, pp. 3569-3572.

Newton Method for the ICA Mixture Model.
Palmer, J.A.; Makeig, S.; Kreutz-Delgado, K.; Rao, B.D. Proceedings of the 33rd IEEE International Conference on Acoustics and Signal Processing (ICASSP 2008), Las Vegas, NV, pp. 1805-1808, 2008.

Modeling and Estimation of Dependent Subspaces with Non-Radially Symmetric and Skewed Densities.
Palmer, J.A.; Kreutz-Delgado, K.; Rao, B.D.; Makeig, S.  Proceedings of the 7th International Symposium on Independent Component Analysis (ICA2007). Edited by Mike E. Davies, Christopher J. James, Samer A. Abdallah and Mark D Plumbley, Lecture Notes in Computer Science, Springer, 2007.

Super-Gaussian Mixture Source Model for ICA.
Palmer, J.A.; Kreutz-Delgado, K.; Makeig, S.  Proceedings of the 6th International Symposium on Independent Component Analysis (ICA2006). Edited by Justinian Rosca, Deniz Erdogmus, Jose C. Principe and Simon Haykin, Lecture Notes in Computer Science, Springer, 2006.

Variational EM Algorithms for Non-Gaussian Latent Variable Models.
Palmer, J.A.; Kreutz-Delgado, K.; Wipf, D.; Rao, B.D. Advances in Neural Information Processing Systems 18 (NIPS 2005). In Proceedings of the 2005 NIPS Conference, Edited by Yair Weiss, Bernhard Scholkopf and John Platt, MIT Press, 2006.

A General Framework for Component Estimation.
Palmer, J.A.; Kreutz-Delgado, K.  4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003). Nara, Japan. April 1-4, 2003.

Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning.
Murray, J.F.; Kreutz-Delgado, K.  Neural Computation. Vol. 19, 2007, pp. 2301-2352.

A Unified FOCUSS Framework for Learning Sparse Dictionaries and Non-Squared Error.
Burdge, B; Kreutz-Delgado, K.; Murray, J.F. Conference Record of the 44th IEEE Asilomar Conference on Signals, Systems and Computers. November 2010, pp. 2037-2041

Learning Sparse Overcomplete Codes for Images.
Murray, J.F.; Kreutz-Delgado, K.  Journal of VLSI Signal Processing. Vol. 45, 2006, pp. 97-110.

Sparse Image Coding Using Learned Overcomplete Dictionaries.
Murray, J.F.; Kreutz-Delgado, K. Proceedings of the 14th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing. Sao Luis, Brazil. Sept.-Oct. 2004, pp. 579-588.

Dictionary Learning Algorithms for Sparse Representation
Kreutz-Delgado, K.; Murray, J.F.; Rao, B.D.; Engan, K.; Lee, T.-W.; Sejnowski, T.  Neural Computation, vol. 15, no. 2. February 2003, pp. 349-396.

An Improved FOCUSS-Based Learning Algorithm for Solving Sparse Linear Inverse Problems.
Murray, J.F.; Kreutz-Delgado, K.  Conference Record of the 35rd IEEE Asilomar Conference on Signals, Systems and Computers. November 2001, pp. 347-351.

FOCUSS-Based Dictionary Learning Algorithms.
Kreutz-Delgado, K.; Rao, B.D. Proceedings of the SPIE Volume 4119: Wavelet Applications in Signal and Image Processing VIII. Vol 4119, pp. 459-453, July-August, 2000.

Frame Design using FOCUSS with Method of Optimal Directions (MOD).
Engan, K.; Rao, B.D.; Kreutz-Delgado, K. NORSIG-99 - Norwegian Signal Processing Symposium. (Asker, Norway, 9-11 Sept. 1999.) Tronheim, Norway: NORSIG, 1999, pp. 65-69.

Novel Algorithms for Learning Overcomplete Dictionaries.
Kreutz-Delgado, K.; Rao, B.D.; Engan, K. Conference Record of the Thirty-Third IEEE Asilomar Conference on Signals, Systems, and Computers. (Pacific Grove, CA, USA, 24-27 Oct. 1999.) Piscataway, NJ, USA: IEEE, 1999, pp. 971-975 Vol. 2.

Parameterized Deformation Sparse Coding Error via Tree-Structured Parameter Search.
Burdge, B; Kreutz-Delgado, K.; Murray, J.F. Conference Record of the 44th IEEE Asilomar Conference on Signals, Systems and Computers. November 2010, pp. 2033-2036

Performance Evaluation of Latent Variable Models with Sparse Priors
Wipf, D.; Palmer, J.A.; Rao, B.D.; Kreutz-Delgado, K. 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (ICASSP'07). Honolulu, HI, USA, 15-20 April 2007, pp. 453-456, Vol. 2.

Sparse Solutions to Linear Inverse Problems with Multiple Measurement Vectors
Cotter, S.F.;Rao, B.D.; Engan, K.; Kreutz-Delgado, K.  IEEE Transactions on Signal Processing, vol. 53, no. 7. July 2005, pp. 2477-2488.

Subset Selection in Noise Based on Diversity Measure Minimization
Rao, B.D.; Engan, K.; Cotter, S.F.; Palmer, J.A.; Kreutz-Delgado, K.  IEEE Transactions on Signal Processing, vol. 51, no. 3. March 2003, pp. 760-770.

A Globally Convergent Algorithm for MAP Estimation in the Linear Model with Non-Gaussian Priors.
Palmer, J.A.; Kreutz-Delgado, K.  Conference Record of the 36rd IEEE Asilomar Conference on Signals, Systems and Computers. November 2002, pp. 1772-1776.

Efficient Backward Elimination Algorithm for Sparse Signal Representation Using Overcomplete Dictionaries.
Cotter, S.F.; Kreutz-Delgado, K.; Rao, B.D. IEEE Signal Processing Letters, vol. 9, no. 5. May 2002, pp. 145-147.

Backward Sequential Elimination Algorithm for Sparse Vector Subset Selection.
Cotter, S.F.; Kreutz-Delgado, K.; Rao, B.D. Signal Processing. Vol. 81, 2001, pp. 1849-1864.

Regularized FOCUSS for Subset Selection in Noise.
Engan, K.; Rao, B.D.; Kreutz-Delgado, K.  NORSIG2000 - Nordic Signal Processing Symposium. (Vildmarkshotellet Kolmarden, Sweden, 13-15 June 2000.) Linkoping, Sweden: Linkoping Univ, 2000, pp. 247-250.

Convex/Schur-Convex (CSC) Log-Priors and Sparse Coding
Kreutz-Delgado, K.; Rao, B.D.; Engan, K.; Lee, T.-W.; Sejnowsk,i T.J.. Proc. 6th Joint Symposium on Neural Computation. Caltech, Pasadena, California. May 1999.

Forward Sequential Algorithms for Best Basis Selection.
Cotter, S.F.; Adler, R.; Rao, R.D.; Kreutz-Delgado, K. IEE Proceedings-Vision, Image and Signal Processing. Vol.146, no.5, IEE (British), Oct. 1999, pp. 235-244.

Sparse Basis Selection, ICA, and Majorization: Towards a Unified Perspective.
Kreutz-Delgado, K.; Rao, B.D. 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (ICASSP99). Phoenix, AZ, USA, 15-19 March 1999, pp.1081-1084, Vol. 2

An Affine Scaling Methodology for Best Basis Selection.
Rao, B.D.; Kreutz-Delgado, K. IEEE Transactions on Signal Processing, vol.47, No. 1. IEEE, Jan. 1999, pp. 187-200.

Application of Concave/Schur-Concave Functions to the Learning of Overcomplete Dictionaries and Sparse Representations.
Kreutz-Delgado, K.; Rao, B.D. Conference Record of 32nd IEEE Asilomar Conference on Signals, Systems and Computers. 1998, pp. 546-550. Vol. 1.

Basis Selection in the Presence of Noise.
Rao, B.D.; Kreutz-Delgado, K. Conference Record of Thirty-Second IEEE Asilomar Conference on Signals, Systems and Computers. 1998, pp. 752-756, Vol.1.

Sparse Solutions to Linear Inverse Problems with Multiple Measurement Vectors.
Rao, B.D.; Kreutz-Delgado, K. Proceedings of the 8th IEEE Digital Signal Processing Workshop. Bryce Canyon, UT. August 1998, pp. 2477-2488.

Improving Spectral Resolution using Basis Selection.
Rao, B.D.; Kreutz-Delgado, K.; Dharanipragada, S. Ninth IEEE Signal Processing Workshop on Statistical Signal and Array Processing. 1998. pp. 336-339.

Measures and Algorithms for Best Basis Selection.
Kreutz-Delgado, K.; Rao, B.D. Proceedings of the 1998 IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP '98). 1998, pp. 1881-1884, Vol. 3.

Deriving Algorithms for Computing Sparse Solutions to Linear Inverse Problems.
Rao, B.D.; Kreutz-Delgado, K. Conference Record of the Thirty-First IEEE Asilomar Conference on Signals, Systems and Computers. IEEE Comput. Soc, 1997, pp. 955-959, Vol.1.

Comparison of Basis Selection Methods.
Adler, J.; Rao, B.D.; Kreutz-Delgado, K. Conference Record of Thirtieth Asilomar Conference on Signals, Systems and Computers. IEEE Comput. Soc. Press, 1996, pp. 252-257, Vol. 1.

Machine Learning and Pattern Recognition in Intelligent Systems & Robotics

Classifying Non-Gaussian and Mixed Data Sets in their Natural Parameter Space.
Levasseur, C; Mayer, U.F.; Kreutz-Delgado, K. IEEE International Workshop on Machine Learning for Signal Processing, 2009, (MLSP 2009). Grenoble, France, September 2-4, 2009, pp. 1-6.

A Unifying Viewpoint of Some Clustering Techniques Using Bregman Divergences and Extensions to Mixed Data Sets.
Levasseur, C.; Burdge, B.; Kreutz-Delgado; Mayer, U.F. Proceedings of the 1st IEEE International  Workshop on Data Mining and Artificial Intelligence (DMAI). December 24, 2008, pp. 56-63

Generalized Statistical Methods for Unsupervised Minority Class Detection in Mixed Data Sets.
Levasseur, C.; Mayer, U.F.; Kreutz-Delgado. Proc. of the 1st International Association for Pattern Recognition (APR) Workshop on Cognitive Information Processing (CIP 2008), Santorini Greece , June 9-10, 2008,  pp. 126-131.

Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application.
Murray, J.F.; Hughes, G.F.; Kreutz-Delgado, K. Journal of Machine Learning Research. Vol 6, no. 1, May 2005, pp. 783-816.

Data-pattern discovery methods for detection in non-Gaussian high-dimensional data sets.
Levasseur, C.; Kreutz-Delgado, K.; Mayer, U.F.; Garganz, G. Conference Record of the Thirty-Ninth IEEE Asilomar Conference on Signals, Systems and Computers. 2005, pp. 545-549.

Improved Disk-Drive Failure Warnings.
Hughes, G.F.; Murray, J.F.; Kreutz-Delgado, K.; Elkan, C. IEEE Transactions on Reliability. Vol 51, no. 3, Sept. 2002, pp. 350-357.

Learning Global Properties of Nonredundant Kinematic Mappings.
DeMers, D.; Kreutz-Delgado, K. International Journal of Robotics Research. Vol 17, no. 5, May 1998, pp. 547-560.

A Grid Algorithm for Autonomous Star Identification.
Padgett, C.; Kreutz-Delgado, K. IEEE Transactions on Aerospace & Electronic Systems. Vol 33, no. 1, Jan. 1997, pp. 202-213.

Canonical Parameterization of Excess Motor Degrees of Freedom with Self-organizing Maps.
DeMers, D.; Kreutz-Delgado, K. IEEE Transactions on Neural Networks. Vol 7, no. 1, Jan. 1996, pp. 43-55.

Learning Global Direct Inverse Kinematics.
DeMers, D.; Kreutz-Delgado, K. Advances in Neural Information Processing Systems (NIPS'93). 1993, pp. 589-594.

Global Regularization of Inverse Kinematics for Redundant Manipulators.
DeMers, D.; Kreutz-Delgado, K. Advances in Neural Information Processing Systems (NIPS'93). (Full plenary talk.) 1993, pp. 255-262.

"Kalman Filter-like" Algorithms that Recursively Solve the Robotic Inverse and Forward Dynamics Problem

Spatial Operator Factorization and Inversion of the Manipulator Mass Matrix.
Rodriguez; G.; Kreutz-Delgado, K. IEEE Transactions on Robotics and Automation. Vol 8, no 1, February 1992, pp. 65-76.

Recursive Formulation of Operational Space Control.
Kreutz-Delgado, K.; Jain, A.; Rodriguez, G. The international Journal of Robotics Research. Vol 11, no 4, August 1992, p. 320-328.

Spatial Operator Algebra for Multibody System Dynamics.
Rodriguez, G.; Jain, A.; Kreutz-Delgado, K. Journal of the Astronautical Sciences. Vol 40, no 1, August 1992, pp. 27-50.

A Spatial Operator Algebra for Manipulator Modeling and Control.
Rodriguez, G.; Jain, A.; Kreutz-Delgado, K. The International Journal of Robotics Research. Vol 10, no 4, August 1991, pp. 371-381.

Use of the Newton Method for Blind Adaptive Equalization based on the Constant Modulus Algorithm.
Kreutz-Delgado, K.; Isukapalli, Y.  IEEE Transactions on Signal Processing. Vol. 56, no. 8. August 2008, pp. 3983-3995.

Frequency Characteristics of Blood Glucose Dynamics.
Gough, D.; Kreutz-Delgado, K.; Bremer, T.M.  Annals of Biomedical Engineering. Vol. 31, 2003, pp. 91-97.

AMICA: An Adaptive Mixture of Independent Component Analyzers with Shared Components.
Palmer, J.A.; Kreutz-Delgado, K.; Makeig, S.   In Preparation.   Draft of September 12, 2011.

Dependency Models based on Generalized Gaussian Scale Mixtures and Normal Variance Mean Mixtures.
Palmer, J.A.; Kreutz-Delgado, K.; Makeig, S.   In Preparation.   Draft of September 7, 2011.

The Complex Gradient Operator and the CR Calculus.
Kreutz-Delgado, K., Arxiv Report, ArXiv:0906.4835v1, June 2009.

A General Approach to Sparse Basis Selection: Majorization, Concavity, and Affine Scaling.
Kreutz-Delgado, K.; Rao, B.D. UCSD ECE Department Technical Report, UCSD-CIE-97-7-1. July 15, 1997.

1) US Patent No. 5303384 (1994), "High Level Language-Based Robotic Control Systems."

2) US Patent No. 5519814 (1996), "High Level Language-Based Robotic Control Systems."

3) US Patent No. 7042928 (2006), "Method and Apparatus for Pilot Estimation Using Prediction Error Method."

4) US Patent No. 7061882 (2006), "Pilot Estimation Using Prediction Error Method-Switched Filters."

5) US Patent No. 7286506 (2007), "Method and Apparatus for Pilot Estimation Using Prediction Error Method with a
    Kalman Filter and a Gauss-Newton Algorithm."

Other patents pending.

Last update: January 30, 2012