CLUES: Cognitive Load Understanding through Experimental Sensing dataset

Understanding Cognitive Load (CL) is es sential to advance our knowledge of human cognitive resources and improve performance in demanding envi ronments. In affective computing, studying CL reveals how cognitive and emotional states interact, influence decision-making, behavior, and well-being. This study presents a comprehensive multi-modal dataset from 55 participants across two sites, examining CLunder relaxed, easy, and difficult task conditions. The dataset features synchronized, high-resolution recordings from multiple devices: Emteq OCOsense smartglasses for facial and head measurements; Empatica E4 wristband for heart rate variability, skin temperature and electro dermal activity; Tobii eye-tracker for gaze dynamics; and camera recordings (depth, RGB, and thermal) for behavioral and thermal measurements. Participants also provided subjective CL ratings, enabling richer analysis by integrating self-reports with sensor data. To validate data quality, the study provides statistical analyses of perceived difficulty and task performance. Machine learning experiments for CL estimation in clude person-dependent, person-independent, and task independent models, highlighting the dataset\u2019s potential for assessing model generalization. Feature importance analysis across models identifies the most informative signals for CL modeling. This resource supports interdis ciplinary research, enabling applications from CL mod eling and machine learning personalization, to adaptive interface design and aims to advance CL assessment through robust, reproducible methods.