TRINA EmotionDrive: Dataset of Emotional Responses Using Six Biosignals and Questionnaires in Driving Simulation

This dataset captures human emotional responses from 30 participants during simulated driving scenarios, using a combination of six physiological signals and self-report questionnaires. The recorded signals include electrocardiogram (ECG), electrodermal activity (EDA), abdominal and chest respiration, skin temperature, and photoplethysmography (PPG). Emotional events were elicited through controlled driving tasks, with participant responses annotated both behaviorally and physiologically. Ground truth was further supported by subjective ratings to validate emotional states such as stress, irritation, and surprise. This multimodal dataset supports research in affective computing, emotion recognition, driver monitoring systems, and the development of machine learning models for real-time emotion detection in naturalistic human\u2013machine interaction settings.