title: Automatic recognition of pleasant content of odours through ElectroEncephaloGraphic activity analysis.
abstract: This study presents a machine learning approach applied to ElectroEnchephaloGraphic (EEG) response in a group of subjects when exposed to a controlled olfactory stimulation experiment. In the literature, in fact, there are controversial results on EEG response to odorants. This study proposes a robust leave-one-subject-out classification method to recognize features extracted from EEG signals belonging to pleasant or unpleasant olfactory stimulation classes. An accuracy of 75% has been achieved in a group of 32 subjects. Moreover a set of features extracted from lateral electrodes emphasized that right and left hemispheres behave differently when the subjects are exposed to pleasant or unpleasant odours stimuli.
authors.forename: Antonio
authors.initials: A
authors.lastname: Lanata
authors.forename: Andrea
authors.initials: A
authors.lastname: Guidi
authors.forename: Alberto
authors.initials: A
authors.lastname: Greco
authors.forename: Gaetano
authors.initials: G
authors.lastname: Valenza
authors.forename: Fabio
authors.initials: F
authors.lastname: Di Francesco
authors.forename: Enzo Pasquale
authors.initials: EP
authors.lastname: Scilingo
mesh_terms: D000328:Adult; D000068598:Data Accuracy; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D009812:Odorants; D055696:Olfactory Perception; D055815:Young Adult