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

要旨

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.

著者
Michael Thomas. Knierim
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Fabio Stano
Karlsruhe Institute of Technology, Karlsruhe, Germany
Fabio Kurz
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Antonius Heusch
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Max L. Wilson
University of Nottingham, Nottingham, United Kingdom
DOI

10.1145/3706598.3713512

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713512

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Biosensing for Interactions

Annex Hall F205
7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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