title: Gender-specific automatic valence recognition of affective olfactory stimulation through the analysis of the electrodermal activity.
abstract: This study reports on the development of a gender-specific classification system able to discern between two valence levels of smell, through information gathered from electrodermal activity (EDA) dynamics. Specifically, two odorants were administered to 32 healthy volunteers (16 males) while monitoring EDA. CvxEDA model was used to process the EDA signal and extract features from both tonic and phasic components. The feature set was used as input to a K-NN classifier implementing a leave-one-subject-out procedure. Results show strong differences in the accuracy of valence recognition between men (62.5%) and women (78%). We can conclude that affective olfactory stimulation significantly affect EDA dynamics with a highly specific gender dependency.
authors.forename: Alberto
authors.initials: A
authors.lastname: Greco
authors.forename: Antonio
authors.initials: A
authors.lastname: Lanata
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; D000465:Algorithms; D001143:Arousal / Q000502:physiology; D001331:Automation; D019992:Databases as Topic; D005260:Female; D005712:Galvanic Skin Response / Q000502:physiology*; D064368:Healthy Volunteers; D006801:Humans; D008297:Male; D010812:Physical Stimulation; D012647:Self-Assessment; D012727:Sex Characteristics; D012903:Smell / Q000502:physiology*; D013223:Statistics as Topic; D011795:Surveys and Questionnaires