Emotion recognition (ER) in human–computer interaction (HCI) holds immense potential for real-world applications, but traditional approaches based on electroencephalography (EEG) face challenges due to the complexity and impracticality of collecting and analyzing EEG data in ambulatory settings. This study explores electrodermal activity (EDA), a simpler measure of the sympathetic nervous system response that can be collected at multiple peripheral body sites, as a potential alternative for ER. We investigated the variable frequency complex demodulation (VFCDM) technique to analyze EDA and EEG signals and used deep learning models (ResNet50 and MobileNetV2) to classify arousal states (high arousal, HA vs. low arousal, LA). Our results show that EDA signals analyzed by VFCDM and classified by MobileNetV2 achieve promising performance, with an accuracy of 91.45%, comparable to the best EEG-based model (91.98%), in arousal classification. This suggests that EDA offers a viable and more practically accessible approach to ER in HCI compared to traditional EEG-based methods. Future work should explore larger and more diverse datasets, incorporate valence classification through multimodal fusion, and investigate the neural mechanisms underlying EDA-EEG interactions during emotional processing to further advance robust ER for HCI applications.
Veeranki et al. (Mon,) studied this question.