Exploring Flow in Real-World Knowledge Work Using Discreet cEEGrid Sensors

TL;DR

This study uses wearable EEG sensors to detect flow states during natural knowledge work, revealing physiological markers and differences from lab-based tasks, advancing real-world flow detection methods.

Contribution

It introduces the use of discreet cEEGrid sensors for in-situ flow detection during natural work, bridging the gap between lab studies and real-world applications.

Findings

Natural work tasks induce more intense flow than artificial tasks.

EEG shows quadratic relationship between theta power and flow.

Novel quadratic relationship between beta asymmetry and flow during real-world tasks.

Abstract

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.