Experiences of studying Attention through EEG in the Context of Review Tasks
Abstract
Context: Electroencephalograms (EEG) have been used in a few cases in the context of software engineering (SE). EEGs allow capturing emotions and cognitive functioning. Such human factors have already shown to be important to understand software engineering tasks. Therefore, it is essential to gain experience in the community to utilize EEG as a research tool. Objective: To report experiences of using EEG in the context of a software engineering education (review of master theses proposals). We provide our reflections and lessons learned of (1) how to plan an EEG study, (2) how to conduct and execute (e.g., tools), (3) how to analyze. Method: We carried out an experiment using an EEG headset to measure the participants' attention rate. The experiment task includes reviewing three master thesis project plans. Results: We describe how we evolved our understanding of experimentation practices to collect and analyze psychological and cognitive data. We also provide a set of lessons learned regarding the application of EEG technology for research. Conclusions: We believe that that EEG could benefit software engineering research to collect cognitive information under certain conditions. The lessons learned reported here should be used as inputs for future experiments in software engineering, where human aspects are of interest.
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The Emotiv EPOC X EEG headset has higher accuracy with 14 channels and validated use in software engineering research for investigating attention in code review tasks.
“We selected this device for its higher accuracy with 14 channels and validated use in software engineering research for investigating attention in code review tasks [20].”
From Towards Decoding Developer Cognition in the Age of AI Assistants (2025) · cites this paper as [20]
Cites
Load Theory of Selective Attention and Cognitive Control
Lavie, N., Hirst, A., de Fockert, J. W., & Viding, E. (2004) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.133.3.339 · Cited by 1,812 · cited 1× · see citing works
“A study in selective attention also suggests that participants are more easily affected by distractions that are more relevant or similar to the task itself [29].”
BackgroundUnderstanding understanding source code with functional magnetic resonance imaging
Siegmund, J., Kästner, C., Apel, S., Parnin, C., Bethmann, A., Leich, T., Saake, G., & Brechmann, A. (2014) · Proceedings of the 36th International Conference on Software Engineering · doi:10.1145/2568225.2568252 · Cited by 219 · see citing works
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Towards Decoding Developer Cognition in the Age of AI Assistants
Haque, E. A., Brown, C., LaToza, T. D., & Johnson, B. (2025) · arXiv · doi:10.48550/arxiv.2501.02684 · Cited by 1 · see citing works
“We selected this device for its higher accuracy with 14 channels and validated use in software engineering research for investigating attention in code review tasks [20].”
- Authors
- Molléri, J. S., Nurdiani, I., Fotrousi, F., & Petersen, K.
- Affiliations
- Simula Metropolitan Center for Digital Engineering (NO)
- University of Southern Denmark (DK)
- Blekinge Institute of Technology (SE)
- Venue
- Proceedings of the Evaluation and Assessment on Software Engineering
- Publisher
- ACM
- Published
- 2019-04-15
- Volume
- 85
- Pages
- 313-318
- OpenAlex
- W2937191320
- Cited by
- 3
- References
- 2
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 1
- FWCI
- 0.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Evaluation and Assessment in Software Engineering (EASE) (publishedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- EEG, attention, software engineering education, mental workload, human factors in software engineering, EEG headset, lessons learned, research planning, EEG signal analysis
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