Assessing Flow State in Virtual Reality: A Multi-Channel Physiological Framework With Self-Supervised Pre-Training

Flow, a state of complete immersion, focus, and enjoyment, has important implications for learning, productivity, and well-being. While research on flow is growing, there remains a need for refined methods to elicit and assess flow states specifically in immersive VR environments. In this work, we address this need by developing Beat Flow, a VR music rhythm game tailored to participants' skill levels, designed to evoke distinct flow states. Instead of using traditional physiological apparatus that limits mobility, we employ lightweight wearable devices to capture electroencephalogram (EEG), galvanic skin response (GSR), photoplethysmography (PPG), and eye blink. Furthermore, we propose a VR-native self-report tool, the 3D Flow Scale (3FS), for efficient flow assessment in VR environments. Besides, we develop a deep learning model with a self-supervised pre-training strategy to classify flow states, achieving 78.4% accuracy within a 10-second time window using 5-fold cross-validation. The model demonstrates state-of-the-art performance, especially in cross-user scenarios, advancing flow detection in VR and paving the way for flow-aware VR applications across various domains.