A multimodal dataset of EEG, eye-tracking, and physiological signals during naturalistic smartphone interactions

Smartphones have become pervasive tools for communication, information consumption, and digital interaction, yet the neurophysiological dynamics associated with naturalistic smartphone use remain insufficiently characterized. Here, we present a multimodal physiological dataset collected during ecologically valid smartphone interaction and a subsequent standardized low-engagement baseline condition. Twenty-three participants engaged with their most frequently used smartphone application (primarily gaming or short form video) for ten minutes, followed by a five-minute passive viewing of a standardized nature video. Simultaneous recordings were obtained from electroencephalography (EEG; 64 channels), wearable eye-tracking, photoplethysmography (PPG), and galvanic skin response (GSR) sensors. Questionnaire-based assessments, including the smartphone addiction scale (SAS) and the mobile phone problematic use scale (MPPUS), are also collected to characterize individual differences in smartphone-related behavioral traits. All data streams are synchronized using transistor-transistor logic (TTL) trigger signals to ensure precise temporal alignment across modalities. The dataset is organized according to the Brain Imaging Data Structure (BIDS) specification and is publicly available on OpenNeuro (Accession Number: ds007537). This dataset enables the investigation of neural, ocular, and autonomic responses during smartphone interaction and supports multimodal analysis of diverse smartphone behaviors while preserving ecological validity.