Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.19217 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909471273385984 |
|---|---|
| author | Knierim, Michael T. Stano, Fabio Kurz, Fabio Heusch, Antonius Wilson, Max L. |
| author_facet | Knierim, Michael T. Stano, Fabio Kurz, Fabio Heusch, Antonius Wilson, Max L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19217 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Flow in Real-World Knowledge Work Using Discreet cEEGrid Sensors Knierim, Michael T. Stano, Fabio Kurz, Fabio Heusch, Antonius Wilson, Max L. Human-Computer Interaction 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. |
| title | Exploring Flow in Real-World Knowledge Work Using Discreet cEEGrid Sensors |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.19217 |