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

TL;DR

This work addresses the challenge of detecting flow in real-world knowledge work by deploying discreet around-the-ear ear-EEG (open-cEEGrid) in a single-day field study with 21 participants performing self-chosen projects and a reference mental arithmetic task. It demonstrates that natural tasks can evoke stronger flow than lab tasks, albeit with smaller contrast, and replicates a convex quadratic relation between theta power and flow in the field, while identifying a novel beta asymmetry relation in natural work. The approach combines rigorous EEG preprocessing with experience sampling and validated self-report measures to enable context-free, in-situ flow monitoring. The findings advance wearable brain sensing for real-time flow detection and open avenues for adaptive work environments and neurofeedback-enabled productivity tools.

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.

Figures (8)

• Figure 1: Visualization of the experiment procedure with screenshots from the various tasks and instructions. Top and bottom show the controlled math task processes, the middle shows the natural work task sessions that were repeated four times.
• Figure 2: Gold-plated open-cEEGrids (A) together with OpenBCI Cyton+Daisy 16-channel biosignal amplifiers (B), worn by a study participant (C), and with examples of one channel's signals with decomposed frequency bands (D).
• Figure 3: The study sample demographics show a relatively young group with balanced gender and project distributions.
• Figure 4: Distributions and test results for project type experience (A), flow proneness with the project (B), and actual flow experience during the study by project type and participant (C). Numbers above bars represent the p-values of the corresponding statistical tests. No significant differences are found in all variables.
• Figure 5: Distribution of flow in the reported activities during the natural work sessions. The majority classes are academic work, programming, and thining/analysis. Numbers above the box plots show the number of observations in total per sub-category and for how many participants the category occurred. Estimated marginal mean differences with p<0.01 are shown with bars.