PARAFAC Analysis of Neural Correlates of Face Detection

Jhoanna Pérez-hidalgo-gato; V. Rodríguez; Eduardo Martínez-montes

doi:10.1007/978-1-4020-8387-7_77

PARAFAC Analysis of Neural Correlates of Face Detection

Jhoanna Pérez-hidalgo-gato, V. Rodríguez, Eduardo Martínez-montes

College of Health and Life Sciences

Research output: Chapter in Book/Published conference output › Other chapter contribution

Abstract

Neural correlates of face processing have been largely studied, but more emphasis has been done in the identification of a particular face. Here we study the neural correlates of the N170 peak corresponding to the correct and incorrect detection of faces through the use of the Bayesian Model Averaging procedure. Moreover, different components of electrical sources are extracted with a PARAFAC analysis of the data. PARAFAC is a generalization of principal component analysis to deal with multidimensional data, offering as a great advantage unique decompositions. PARAFAC analysis of the three-dimensional data formed by the array of BMA inverse solutions for each subject and each experimental condition, provide of characteristic BMA sources with corresponding profiles for subjects and conditions. This allowed the identification of different and common sources for correct and incorrect detection of faces.

Original language	English
Title of host publication	Advances in Cognitive Neurodynamics ICCN 2007
Editors	Rubin Wang, Enhua Shen, Fanji Gu
Publisher	Springer
Chapter	77
Pages	447-450
ISBN (Electronic)	978-1-4020-8387-7
ISBN (Print)	978-1-4020-8386-0
DOIs	https://doi.org/10.1007/978-1-4020-8387-7_77
Publication status	Published - 2008

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10.1007/978-1-4020-8387-7_77

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title = "PARAFAC Analysis of Neural Correlates of Face Detection",

abstract = "Neural correlates of face processing have been largely studied, but more emphasis has been done in the identification of a particular face. Here we study the neural correlates of the N170 peak corresponding to the correct and incorrect detection of faces through the use of the Bayesian Model Averaging procedure. Moreover, different components of electrical sources are extracted with a PARAFAC analysis of the data. PARAFAC is a generalization of principal component analysis to deal with multidimensional data, offering as a great advantage unique decompositions. PARAFAC analysis of the three-dimensional data formed by the array of BMA inverse solutions for each subject and each experimental condition, provide of characteristic BMA sources with corresponding profiles for subjects and conditions. This allowed the identification of different and common sources for correct and incorrect detection of faces.",

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AU - Pérez-hidalgo-gato, Jhoanna

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AU - Martínez-montes, Eduardo

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N2 - Neural correlates of face processing have been largely studied, but more emphasis has been done in the identification of a particular face. Here we study the neural correlates of the N170 peak corresponding to the correct and incorrect detection of faces through the use of the Bayesian Model Averaging procedure. Moreover, different components of electrical sources are extracted with a PARAFAC analysis of the data. PARAFAC is a generalization of principal component analysis to deal with multidimensional data, offering as a great advantage unique decompositions. PARAFAC analysis of the three-dimensional data formed by the array of BMA inverse solutions for each subject and each experimental condition, provide of characteristic BMA sources with corresponding profiles for subjects and conditions. This allowed the identification of different and common sources for correct and incorrect detection of faces.

AB - Neural correlates of face processing have been largely studied, but more emphasis has been done in the identification of a particular face. Here we study the neural correlates of the N170 peak corresponding to the correct and incorrect detection of faces through the use of the Bayesian Model Averaging procedure. Moreover, different components of electrical sources are extracted with a PARAFAC analysis of the data. PARAFAC is a generalization of principal component analysis to deal with multidimensional data, offering as a great advantage unique decompositions. PARAFAC analysis of the three-dimensional data formed by the array of BMA inverse solutions for each subject and each experimental condition, provide of characteristic BMA sources with corresponding profiles for subjects and conditions. This allowed the identification of different and common sources for correct and incorrect detection of faces.

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M3 - Other chapter contribution

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EP - 450

BT - Advances in Cognitive Neurodynamics ICCN 2007

A2 - Wang, Rubin

A2 - Shen, Enhua

A2 - Gu, Fanji

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PARAFAC Analysis of Neural Correlates of Face Detection

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