Classification of signals using wavelets and principal components reduction, with application to auditory brain activity.

Irène Gannaz (1)

1 ICJ - Institut Camille Jordan

The paper deals with a generalized linear model with functional data using a wavelet representation of the signals. A reduction of dimension is first obtained through a principal component analysis. The discriminative function is then given by a loglikelihood maximization, with a LASSO penalization, in order to ensure the sparsity of the wavelet representation. In order to have a data-driven procedure, we explore different cross-validation schemes. A simulation study is presented, showing our estimator that is competitive with those described in Reiss and Ogden (2013). We apply this model to a classification of functional EEG data, to study the capacity of discrimination of nearby sounds.

Mots clés: Cross-validation; Generalized functional linear model; Curves classification; Wavelets; Principal component reduction; LASSO penalization; Cross-validation.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Irène Gannaz. Classification of signals using wavelets and principal components reduction, with application to auditory brain activity.. 2013. ⟨hal-00830313v1⟩