2026-10-13T00:23:30Z https://www.scidb.cn/oai

10.57760/sciencedb.37907 2026-06-01T17:47:22Z

2026-06-01 A Multimodal EEG–Source–Vehicle Dynamics Dataset for Road-Rage-Related Affect Recognition in Real-Road Traffic Conflicts doi:10.57760/sciencedb.37907 en This dataset supports the study of road-rage-related affect recognition under real-road traffic-conflict scenarios. The data were collected in a real vehicle environment, where participants experienced two types of traffic-conflict induction scenarios, including longitudinal threat and lateral intrusion, under the control of a professional safety driver. The dataset contains synchronized 32-channel scalp EEG signals, source-space brain-region time series reconstructed from scalp EEG, vehicle dynamics signals, and four-level subjective road-rage-related affect labels. The vehicle dynamics signals include vehicle speed, longitudinal acceleration, lateral acceleration, and yaw rate. All samples were temporally aligned using unified timestamps and assigned a four-level affective label, where 0 denotes no road rage, 1 denotes mild irritation, 2 denotes moderate anger, and 3 denotes severe anger.To protect participant privacy, the dataset has been anonymized and does not contain participant names, contact information, facial images, in-cabin videos, or other personally identifiable information. The dataset is intended for manuscript review, method reproducibility, model validation, and result verification. road rage; EEG; source-space EEG; vehicle dynamics; multimodal fusion; affect recognition; real-road traffic conflict; intelligent vehicle Yuchen Guo PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank