Classification of neural signals from sparse autoregressive features
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[1] Yi Lin. Multicategory Support Vector Machines, Theory, and Application to the Classification of . . . , 2003 .
[2] Robert Tibshirani,et al. The Entire Regularization Path for the Support Vector Machine , 2004, J. Mach. Learn. Res..
[3] P. Bühlmann,et al. The group lasso for logistic regression , 2008 .
[4] Z. Keirn,et al. A new mode of communication between man and his surroundings , 1990, IEEE Transactions on Biomedical Engineering.
[5] Timo Similä,et al. Common Subset Selection of Inputs in Multiresponse Regression , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[6] Samy Bengio,et al. HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems , 2004, ESANN.
[7] M J Stokes,et al. EEG-based communication: a pattern recognition approach. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[8] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[9] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[10] H. Akaike. A new look at the statistical model identification , 1974 .
[11] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[12] D M Durand,et al. Suppression of axonal conduction by sinusoidal stimulation in rat hippocampus in vitro , 2007, Journal of neural engineering.
[13] R. Tibshirani,et al. A note on the group lasso and a sparse group lasso , 2010, 1001.0736.
[14] Nan-Jung Hsu,et al. Subset selection for vector autoregressive processes using Lasso , 2008, Comput. Stat. Data Anal..
[15] Klaus Nordhausen,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .
[17] Hans C. van Houwelingen,et al. The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, New York, 2001. No. of pages: xvi+533. ISBN 0‐387‐95284‐5 , 2004 .
[18] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[19] G. Wahba,et al. Multicategory Support Vector Machines , Theory , and Application to the Classification of Microarray Data and Satellite Radiance Data , 2003 .
[20] Ramaswamy Palaniappan,et al. Neural network classification of autoregressive features from electroencephalogram signals for brain–computer interface design , 2004, Journal of neural engineering.
[21] Bin He,et al. Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns , 2004, Clinical Neurophysiology.
[22] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[23] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[24] J. Cadzow. Maximum Entropy Spectral Analysis , 2006 .
[25] J. Franklin,et al. The elements of statistical learning: data mining, inference and prediction , 2005 .