CTFear: A Fear Emotion Intensity Classification Method Based on EEG and Real-Time Labeling in Virtual Environment

Fear plays a crucial role in human behavior and psychological responses. However, accurately quantifying its intensity remains challenging. To address this, we proposed a labeling paradigm in virtual environments: while watching immersive VR videos, users continuously pressed the VR controller trigger to map their subjective fear intensity to a real-time continuous label. Immersive VR videos were utilized as the experimental scenes because they represent a controllable and easily standardized intervention in exposure therapy. To enhance labeling accuracy, haptic vibration cues were provided at preset trigger depth thresholds, serving as physical anchors that allowed subjects to distinguish between fear levels without visual confirmation. Building on this, we designed CTFear, which combined parallel convolutional neural networks with spatial and temporal Transformers and introduced a topology-aware spatial positional encoding to integrate cross-electrode information. Experimental results showed that CTFear achieved average F1 scores of 0.86,0.76, and 0.67 for the two-class, three-class, and four-class classification of fear intensity in cross-trial validation, respectively, and 0.80, 0.64, and 0.55 in cross-subject validation, outperforming various existing methods in multiple classification of fear intensity tasks. This indicated our method can effectively drive EEG decoding of fear intensity and offers a viable pathway for emotion monitoring and interaction in virtual environment.