DSRP: A Database for Stress Reduction Using Physiological Signals

Psychological stress represents a prevalent societal concern, particularly evident during crises such as epidemics, which exacerbate various forms of psychological strain. Prompt mitigation of psychological stress holds potential for enhancing quality of life and fostering societal advancement. In this study, drawing inspiration from the International Emotion Picture Library, we crafted four virtual reality (VR) scenes augmented with five emblematic animals, resulting in 20 VR scenarios for psychological stress assessment. Fifteen participants engaged in the test, which was preceded by the completion of the World Health Organization Quality of Life Questionnaire (WHOQOL-BREF). Throughout the test, EEG and heart rate data were meticulously recorded. Following each scenario, participants completed the Self-Assessment Manikin (SAM) scale, culminating in the formation of the VR psychological stress reduction dataset. We meticulously analyze the SAM scale. For EEG data, we devised a deep learning model leveraging a hybrid CNN-LSTM architecture for signal classification. The model was evaluated on both the publicly available DEAP dataset and our collected DSRP dataset, yielding superior performance. Leveraging the dataset amassed in this study, we scrutinize the alleviative impact of VR on human psychological stress, providing foundational insights for VR-based psychological stress reduction research. Moreover, the EEG classification model proposed herein enhances the categorization of EEG signals, thereby advancing the domain of emotion recognition grounded in EEG technology.