Using the β/α Ratio to Enhance Odor-Induced EEG Emotion Recognition

Abstract

Emotion recognition using an odor-induced electroencephalogram (EEG) has broad applications in human-computer interaction. However, existing studies often rely on subjective self-reporting to label emotion, lacking objective verification. While the β/α ratio has been identified as a potential objective indicator of arousal in EEG spectral analysis, its value in emotion recognition remains underexplored. This study ensured the authenticity of emotions through self-reporting and EEG spectral analysis of 50 adults after inhaling sandalwood essential oil (SEO) or bergamot essential oil (BEO). Classification models were built using discriminant analysis (DA), support vector machine (SVM), and random forest (RF) algorithms to identify low or high arousal emotions. Notably, this study introduced the β/α ratio as a novel frequency domain feature to enhance model performance for the first time. Both self-reporting and EEG spectral analysis indicated that SEO promotes relaxation, whereas BEO enhances attentiveness. In model testing, incorporating the β/α ratio enhanced the performance of all models, with the accuracy of DA, SVM, and RF increasing from 70%, 75%, and 85% to 75%, 80%, and 95%, respectively. This study validated the authenticity of emotions by employing a combination of subjective and objective methods and highlighted the importance of β/α in emotion recognition along the arousal dimension.

Keywords

• olfactory stimulation
• EEG
• machine learning
• emotion recognition
• β/α ratio