PERCEIV: A Multimodal Physiological Dataset of Visual Encodings for Adaptive InfoVis Interfaces

We contribute PERCEIV (PERCeption of visual Encodings in InfoVis), a large-scale dataset to support studies of user responses to visual encoding variants as a foundation for adaptive information visualization interfaces. The dataset includes recordings from 120 participants who completed tasks of varying difficulty, while simultaneous multimodal data were collected from brain, eye, and electrodermal activity. All data are time-synchronized and accompanied by relevant annotations and participant metadata. We release raw sensor streams together with fully processed event-aligned derivatives and rich trial-level annotations. We also provide baseline analyses and scripts for ex tracting cognitive load features across modalities. In addition, we make the code for data pre-processing, feature extraction, qual ity control, and training/evaluating baseline Machine Learning classifiers available. Standardized data splits and comprehensive documentation further enable reproducible benchmarking and rapid reuse. We hope our dataset will facilitate novel studies of sensor modalities, development of computational models, and deeper understanding of cognitive state dynamics in controlled information-processing contexts.