Abstract
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.
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Vega-Hernández, M., Martínez-Montes, E., Pérez-Hidalgo-Gato, J., Sánchez-Bornot, J.M., Valdés-Sosa, P. (2008). Penalized Regression Methods in the Source Analysis of Face Recognition. In: Wang, R., Shen, E., Gu, F. (eds) Advances in Cognitive Neurodynamics ICCN 2007. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8387-7_107
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DOI: https://doi.org/10.1007/978-1-4020-8387-7_107
Publisher Name: Springer, Dordrecht
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