Penalized Regression Methods in the Source Analysis of Face Recognition

  • Mayrim Vega-Hernández
  • Eduardo Martínez-Montes
  • Jhoanna Pérez-Hidalgo-Gato
  • José M. Sánchez-Bornot
  • Pedro Valdés-Sosa
Conference paper


Recent developments in the field of variable selection through penalized least squares regression provide means for the analysis of neuroscience data. Particularly, combinations of non-convex penalties allow for sparse solutions and other unexplored properties that are especially attractive in their application to e.g. EEG/MEG inverse problem. Here, we explore the use of these techniques for the source analysis of a cognitive process, namely, the recognition of faces. Found sources are in agreement with previous studies and new methods, based on combination of penalties, provided for more physiologically plausible solutions.


Multiple penalized least squares EEG inverse problem 


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  1. 1.
    Pascual-Marqui, R.D.: Review of methods for solving the EEG inverse problem. Int. J. Bioelectromagnetism 1 (1999) 75–86.Google Scholar
  2. 2.
    Fan, J.Q. and Li, R.Z.: Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc. 96 (2001) 1348–1360.CrossRefGoogle Scholar
  3. 3.
    Hunter, D.R. and Lange, K.: A tutorial on MM algorithms. Am. Stat. 58 (2004) 30–37.CrossRefGoogle Scholar
  4. 4.
    Valdés-Sosa, P.A.; Sánchez-Bornot, J.M.; Vega-Hernández, M.; Melie-Garcia, L.; Lage-Castellanos, A. and Canales-Rodriguez, E.: Granger Casuality on Spatial Manifolds: applications to Neuroimaging. Handbook of Time Series Analysis: Recent Theoretical Developments and Applications. Chapter 18 (2006). ISBN: 3-527-40623-9.Google Scholar
  5. 5.
    Vega-Hernández, M., Sánchez-Bornot, J.M., Lage-Castellanos, A., Martínez-Montes, E. and Valdés-Sosa, P.A.: Penalized regression methods for solving the EEG inverse problem. Available on CD-Rom in Neuroimage 27 1 (2006).Google Scholar
  6. 6.
    Golub, G., Heath, M. and Wahba, G.: Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21 (1979) 215–223.CrossRefGoogle Scholar
  7. 7.
    Kanwisher, N., McDermott, J. and Chon, M.M.: The fusiform area: a module in human extrastriate cortex specialized for face perception. J. Neurosci. 17 (1997) 4302–4311.PubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mayrim Vega-Hernández
    • 1
  • Eduardo Martínez-Montes
  • Jhoanna Pérez-Hidalgo-Gato
  • José M. Sánchez-Bornot
  • Pedro Valdés-Sosa
  1. 1.Neurostatistics DepartmentCuban Neuroscience CenterCuba

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