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.
|Title of host publication||Advances in Cognitive Neurodynamics ICCN 2007|
|Editors||Rubin Wang, Enhua Shen, Fanji Gu|
|Publication status||Published - 2008|