Reproducible Physiological Features in Affective Computing: A Preliminary Analysis on Arousal Modeling
This addresses the problem of reproducibility in physiological feature selection for researchers and practitioners in Affective Computing, particularly for safety-critical applications like mental disorder recognition, though it is preliminary and incremental.
The study tackled the challenge of linking subjective emotional experiences with objective physiological markers in Affective Computing by identifying reproducible physiological features associated with arousal levels, finding that only two electrodermal-derived features showed statistically significant associations with a 100% confirmation rate.
In Affective Computing, a key challenge lies in reliably linking subjective emotional experiences with objective physiological markers. This preliminary study addresses the issue of reproducibility by identifying physiological features from cardiovascular and electrodermal signals that are associated with continuous self-reports of arousal levels. Using the Continuously Annotated Signal of Emotion dataset, we analyzed 164 features extracted from cardiac and electrodermal signals of 30 participants exposed to short emotion-evoking videos. Feature selection was performed using the Terminating-Random Experiments (T-Rex) method, which performs variable selection systematically controlling a user-defined target False Discovery Rate. Remarkably, among all candidate features, only two electrodermal-derived features exhibited reproducible and statistically significant associations with arousal, achieving a 100\% confirmation rate. These results highlight the necessity of rigorous reproducibility assessments in physiological features selection, an aspect often overlooked in Affective Computing. Our approach is particularly promising for applications in safety-critical environments requiring trustworthy and reliable white box models, such as mental disorder recognition and human-robot interaction systems.