Olfactory Affective Computation Based on EEG Signal Data

It is well known that the sense of smell has significant impacts on human moods, therefore olfactory effects have been widely applied to psychological adjustment as well as clinical treatment. Unlike other senses, smell works through the molecules of olfactory stimuli acting on the human nervous system to elicit psychological effects, which is difficult to be accurately described and measured. This makes the commonly used methods hardly applicable to olfactory affective computation. Through analysis of the neural mechanism of human emotions evoked by olfactory sense, this paper specifically designed an EEG experiment to obtain the neural activity data of olfactory stimuli, and compares the clustering characteristics of neural feature data with self-reported scores in PAD emotional space. Thereout, the LS-SVR estimator based on the feature parameters extracted from EEG signal data is proposed for olfactory affective computation. It shows better distinguishing performance and potential reliability than self-reported data, and thus provides an enlightening exploration of this issue.