Emotion Estimation Using Single-Channel EEG and Heart Rate Variability for Industrial Applications

The industrial world is shifting from mass production to meeting individual needs, requiring guidelines to incorporate emotions and preferences. Traditional questionnaires fall short in capturing detailed real-time emotion changes. Therefore, we propose a novel approach using Electroencephalography(EEG), which reflects central nervous system activity, and heart rate variability(HRV), which reflects autonomic nervous system activity, to estimate emotions in real-time by analyzing arousal and valence. Based on the 2D Arousal-Valence model, we plot EEG and HRV data onto this model to visualize emotion changes caused by external stimuli. Using this proposed method, we conducted joint research with the home and personal care sector to evaluate emotional responses to aroma products, and with the automotive sector to assess drivers' emotional states. These studies demonstrate the value of academic research in guiding the industrial sector, particularly through emotion estimation based on EEG and HRV. Our findings suggest that these technologies offer valuable insights into consumer emotions, which can help improve product design and user experience. Ongoing collaboration between academia and the industrial sector is crucial for overcoming challenges in interpreting physiological signals and effectively integrating these methods into practice.