MPD-DF: Multimodal Phenotyping Dataset of Driving Fatigue -- The Preprocessed Dataset

Driving fatigue analysis using multimodal physiological signals has gained significant attention in human factor engineering. We established a publicly available Multimodal Phenotyping Dataset of Driving Fatigue (MPD-DF) from 50 participants using a standardized 2-hour driving simulation. This meticulously curated dataset incorporated multidimensional subjective and objective metrics for fatigue assessment, including multimodal physiological recordings (32-channel electroencephalogram, single-lead electrocardiogram, dual-channel electrooculogram, and thoracic respiratory effort signals). The supplementary metadata encompassed fatigue-associated questionnaire assessment results along with fatigue level annotations by an expert physician. The validity of the dataset was rigorously verified through multiple dimensions: (1) efficacy of fatigue induction, (2) statistical analysis of questionnaire results, (3) evaluation of signal quality, and (4) correlation between physiological signals and fatigue levels. This systematically validated dataset supports fatigue-related algorithm development, cross-dataset model validation, and investigation of fatigue mechanisms, thereby providing essential resources for transportation safety research.