Flow, a state of deep task engagement, is associated with optimal experience and well-being, making its detection a prolific HCI research focus. While physiological sensors show promise for flow detection, most studies are lab-based. Furthermore, brain sensing during natural work remains unexplored due to the intrusive nature of traditional EEG setups. This study addresses this gap by using wearable, around-the-ear EEG sensors to observe flow during natural knowledge work, measuring EEG throughout an entire day. In a semi-controlled field experiment, participants engaged in academic writing or programming, with their natural flow experiences compared to those from a classic lab paradigm. Our results show that natural work tasks elicit more intense flow than artificial tasks, albeit with smaller experience contrasts. EEG results show a well-known quadratic relationship between theta power and flow across tasks, and a novel quadratic relationship between beta asymmetry and flow during complex, real-world tasks.
@article{arxiv.2501.19217,
title = {Exploring Flow in Real-World Knowledge Work Using Discreet cEEGrid Sensors},
author = {Michael T. Knierim and Fabio Stano and Fabio Kurz and Antonius Heusch and Max L. Wilson},
journal= {arXiv preprint arXiv:2501.19217},
year = {2025}
}