Researchers' modular workflow syncs eye tracking and EEG for reading studies

The paper, presented at KES 2026, describes a reusable web-based workflow designed for real-time multimodal experiments in naturalistic online reading. Built by researchers including Thomas Krämer and Daniel Hienert, the system integrates synchronized eye tracking, EEG, and interaction data using Lab Streaming Layer (LSL), a standard for time-synchronized data collection. Crucially, the workflow anchors gaze data to browser-rendered text at multiple granularities—word, sentence, and areas of interest (AOIs)—allowing researchers to compute gaze-derived metrics online during sessions. EEG and other streams are processed immediately after each task using fixation-triggered segmentation, meaning neural signals are aligned precisely with what the participant was reading.

As a proof of concept, the team applied the workflow to a study of selective exposure in online news search and reading. Participants browsed real news content while their eye movements, brain activity, and keyboard/mouse actions were tracked. The synchronized data enables researchers to select specific text passages for targeted post-task rating or labeling within the same lab session, eliminating the need for delayed self-reports that can be biased by memory. The workflow is explicitly designed to be modular and adaptable, supporting different sensors, tasks, and analysis goals. This makes it a practical tool for HCI researchers, cognitive scientists, and UX professionals aiming to validate constructs like attention, engagement, and comprehension in ecological contexts rather than relying solely on self-report questionnaires.