Odor-induced emotion recognition based on average frequency band division of EEG signals

• Published in: Journal of neuroscience methods 2020-03, Vol.334, p.108599-108599, Article 108599
• Main Authors: Hou, Hui-Rang, Zhang, Xiao-Nei, Meng, Qing-Hao
• Format: Article
• Language: English
• Subjects: Average frequency band division; Emotion recognition; Machine learning; Olfactory EEG
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2020.108599

•A dataset of olfactory EEG signals induced by thirteen odors is established.•An AFBD method was used to extract the PSD features from EEG signals.•The SVM plus AFBD method represents a useful contribution to olfactory-induced emotion recognition.•It is suggested that the recognition performance decreased as the number of emotions in the category increased. Emotion recognition plays a key role in multimedia. To enhance the sensation of reality, smell has been incorporated into multimedia systems because it can directly stimulate memories and trigger strong emotions. For the recognition of olfactory-induced emotions, this study explored a combination method using a support vector machine (SVM) with an average frequency band division (AFBD) method, where the AFBD method was proposed to extract the power-spectral-density (PSD) features from electroencephalogram (EEG) signals induced by smelling different odors. The so-called AFBD method means that each PSD feature was calculated based on equal frequency bandwidths, rather than the traditional EEG rhythm-based bandwidth. Thirteen odors were used to induce olfactory EEGs and their corresponding emotions. These emotions were then divided into two types of emotions, pleasure and disgust, or five types of emotions that were very unpleasant, slightly unpleasant, neutral, slightly pleasant, and very pleasant. Comparison between the proposed SVM plus AFBD method and other methods found average accuracies of 98.9 % and 88.5 % for two- and five-emotion recognition, respectively. These values were considerably higher than those of other combination methods, such as the combinations of AFBD or EEG rhythm-based features with naive Bayesian, k-nearest neighbor classification, voting-extreme learning machine, and backpropagation neural network methods. The SVM plus AFBD method represents a useful contribution to olfactory-induced emotion recognition. Classification of the five-emotion categories was generally inferior to the classification of the two-emotion categories, suggesting that the recognition performance decreased as the number of emotions in the category increased.