Stress Detection in the Wild: On the Impact of Cross-Training on Mental State Detection

This research is concerned with the following question: How a model trained on mental state detection using stress inducer X would perform on detecting a stress stimulated by inducer Y? This is a scenario that can be faced often in real-life. In vision applications, detection in the wild usually implies a real-life setting with minimal constraints. In this research, we investigate the generalization between a classical stress inducer, namely, mental arithmetic stressor (MAE), and the stress induced by immersive technologies, particularly virtual reality (VR)-VR-induced stress. On a group of three participants, we acquired EEG recordings following their exposure to the two types of stressors mentioned above. Using multiple machine learning (ML) classifiers, we show that even basic ML models, when trained to differentiate between the MAE stressor and the non-stress state, can generalize well when tested on differentiating between VR-based stress and the non-stress state. Significant cross-detection performance is reported using accuracy, precision, recall, and F1-score. This research provides preliminary insights on the capacity of machine learning models to generalize well in the contexts of mental state detection. 1