Although we have multiple senses, multimedia mainly targets vision and olfaction. To expand the senses impacted by multimedia, olfactory stimulation has been used to enhance the sense of reality. Odors are primarily matched with objects in scenes. However, it is impractical to select all odors that match all objects in a scene and offer them to viewers. As an alternative, offering a single odor in a category as representative of other odors belonging to that category has been suggested. However, it is unclear whether viewers' responses to videos with multiple odors (e.g., rose, lavender, and lily) from a category (e.g., flowers) are comparable. Therefore, we studied whether odors belonging to a given category could be similar in behavioral congruency and in the five frequency bands (delta, theta, alpha, beta, and gamma) of electroencephalogram (EEG) data collected while viewers watched videos. We conducted questionnaires and EEG experiments to understand the effects of similar odors belonging to categories. Our results showed that similar odors in a specific odor category were more congruent with videos than those in different odor categories. In our EEG data, the delta and theta bands were mainly clustered when odors were offered to viewers in similar categories. The theta band is known to be primarily related to the neural signals of odor information. Our studies showed that choosing odors based on odor categories in multimedia can be feasible.
Keywords: Cluster analysis; EEG; Machine learning; Multimedia; Olfactory perception.