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.

Automatic recognition of pleasant content of odours through ElectroEncephaloGraphic activity analysis

LANATA', ANTONIO;GUIDI, ANDREA;GRECO, ALBERTO;VALENZA, GAETANO;DI FRANCESCO, FABIO;SCILINGO, ENZO PASQUALE
2016-01-01

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.
978-1-4577-0220-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/812440
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