Using fNIRS brain sensing to evaluate information visualization interfaces
Abstract
We show how brain sensing can lend insight to the evaluation of visual interfaces and establish a role for fNIRS in visualization. Research suggests that the evaluation of visual design benefits by going beyond performance measures or questionnaires to measurements of the user's cognitive state. Unfortunately, objectively and unobtrusively monitoring the brain is difficult. While functional near-infrared spectroscopy (fNIRS) has emerged as a practical brain sensing technology in HCI, visual tasks often rely on the brain's quick, massively parallel visual system, which may be inaccessible to this measurement. It is unknown whether fNIRS can distinguish differences in cognitive state that derive from visual design alone. In this paper, we use the classic comparison of bar graphs and pie charts to test the viability of fNIRS for measuring the impact of a visual design on the brain. Our results demonstrate that we can indeed measure this impact, and furthermore measurements indicate that there are not universal differences in bar graphs and pie charts.
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“We followed the PRISMA procedures [121,174] for systematic reviews and meta-analyses.”
From A Survey on Measuring Cognitive Workload in Human-Computer Interaction (2023) · cites this paper as [174]
Cites
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
(1988) · Advances in Psychology · doi:10.1016/s0166-4115(08)62386-9 · Cited by 15,003 · see citing works
N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies.
Owen, A. M., McMillan, K. M., Laird, A. R., & Bullmore, E. (2005) · Human Brain Mapping · doi:10.1002/hbm.20131 · Cited by 3,560 · see citing works
Displaying proportions and percentages
Spence, I., & Lewandowsky, S. (1991) · Applied Cognitive Psychology · doi:10.1002/acp.2350050106 · Cited by 162 · see citing works
Benefitting InfoVis with Visual Difficulties
Hullman, J., Adar, E., & Shah, P. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.175 · Cited by 159 · see citing works
Brainput: enhancing interactive systems with streaming fnirs brain input
Solovey, E., Schermerhorn, P., Scheutz, M., Sassaroli, A., Fantini, S., & Jacob, R. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208372 · Cited by 86 · see citing works
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A Survey on Measuring Cognitive Workload in Human-Computer Interaction
Kosch, T., Karolus, J., Zagermann, J., Reiterer, H., Schmidt, A., & Woźniak, P. W. (2023) · ACM Computing Surveys · doi:10.1145/3582272 · Cited by 304 · see citing works
Yuksel, B. F., Oleson, K. B., Harrison, L., Peck, E. M., Afergan, D., Chang, R., & Jacob, R. J. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858388 · Cited by 137 · see citing works
“Due to its general ease in setting up with users and its relative tolerance of minor motion, fNIRS is an increasingly popular method of brain sensing in the HCI community [43, 4, 44, 36, 1, 2, 19].”
MethodSelf-citation
- Authors
- Peck, E. M. M., Yuksel, B. F., Ottley, A., Jacob, R. J. K., & Chang, R.
- Affiliations
- Tufts University (US)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2013-04-27
- Pages
- 473-482
- OpenAlex
- W2081341882
- Cited by
- 113
- References
- 5
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 9 · 2022: 4 · 2021: 7 · 2020: 12 · 2019: 11 · 2018: 13 · 2017: 11 · 2016: 13 · 2015: 8 · 2014: 12 · 2013: 1
- FWCI
- 7.23
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- functional near-infrared spectroscopy, information visualization, bar charts, pie charts, visual system
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