Brainatwork: logging cognitive engagement and tasks in the workplace using electroencephalography
Abstract
Today's workplaces are dynamic and complex. Digital data sources such as email and video conferencing aim to support workers but also add to their burden of multitasking. Psychophysiological sensors such as Electroencephalography (EEG) can provide users with cues about their cognitive state. We introduce BrainAtWork, a workplace engagement and task logger which shows users their cognitive state while working on different tasks. In a lab study with eleven participants working on their own real-world tasks, we gathered 16 hours of EEG and PC logs which were labeled into three classes: central, peripheral and meta work. We evaluated the usability of BrainAtWork via questionnaires and interviews. We investigated the correlations between measured cognitive engagement from EEG and subjective responses from experience sampling probes. Using random forests classification, we show the feasibility of automatically labeling work tasks into work classes. We discuss how BrainAtWork can support workers on the long term through encouraging reflection and helping in task scheduling.
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Hassib et al. used Electroencephalography to classify the task type according to cognitive load, but without looking at specific task types.
“Hassib et al. [34] who used Electroencephalography to classify the task type according to cognitive load, but without looking at specific task types.”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [34]
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Detecting Developers' Task Switches and Types
Meyer, A. N., Satterfield, C., Zuger, M., Kevic, K., Murphy, G. C., Zimmermann, T., & Fritz, T. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2984086 · Cited by 14 · see citing works
“[34] who used Electroencephalography to classify the task type according to cognitive load, but without looking at specific task types.”
Method
- Authors
- Hassib, M., Khamis, M., Friedl, S., Schneegass, S., & Alt, F.
- Affiliations
- University of Stuttgart (DE)
- Ludwig-Maximilians-Universität München (DE)
- University of Duisburg-Essen (DE)
- Venue
- Proceedings of the 16th International Conference on Mobile and Ubiquitous Multimedia
- Publisher
- ACM
- Published
- 2017-11-26
- Pages
- 305-310
- OpenAlex
- W2774574305
- Cited by
- 24
- References
- 20
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 4 · 2022: 2 · 2021: 2 · 2020: 4 · 2019: 7
- FWCI
- 4.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Mobile and Ubiquitous Multimedia (MUM) (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
- Funders
- Bavarian State Ministry of Education, Science and the Arts
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- EEG, mental state monitoring, multitasking, digital work tools, ecological momentary assessment, usability evaluation, task scheduling, reflection support
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