Brainput: enhancing interactive systems with streaming fnirs brain input
Abstract
This paper describes the Brainput system, which learns to identify brain activity patterns occurring during multitasking. It provides a continuous, supplemental input stream to an interactive human-robot system, which uses this information to modify its behavior to better support multitasking. This paper demonstrates that we can use non-invasive methods to detect signals coming from the brain that users naturally and effortlessly generate while using a computer system. If used with care, this additional information can lead to systems that respond appropriately to changes in the user's state. Our experimental study shows that Brainput significantly improves several performance metrics, as well as the subjective NASA-Task Load Index scores in a dual-task human-robot activity.
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fNIRS has been demonstrated by HCI researchers to measure user cognitive workload in adaptive systems and is resilient to subject movement such as playing piano.
“Brain sensing using functional near-infrared spectroscopy (fNIRS) has recently been demonstrated by HCI researchers to measure user cognitive workload in adaptive systems [4,44,1] and is resilient to subject movement as might be seen while playing a piano [43].”
From Learn Piano with BACh: An Adaptive Learning Interface that Adjusts Task Difficulty Based on Brain State (2016) · cites this paper as [44]
Studies in HCI have successfully used fNIRS to measure and adapt to cognitive workload in real-time.
“Studies in HCI have successfully used fNIRS to measure and adapt to cognitive workload in real-time [44,1,2,55].”
From Learn Piano with BACh: An Adaptive Learning Interface that Adjusts Task Difficulty Based on Brain State (2016) · cites this paper as [44]
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Yuksel, B. F., Peck, E. M., Afergan, D., Hincks, S. W., Shibata, T., Kainerstorfer, J. M., Tgavalekos, K. T., Sassaroli, A., Fantini, S., & Jacob, R. J. K. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2075929 · Cited by 5 · see citing works
Self-citationAugmenting spatial skills with semi-immersive interactive desktop displays
Solovey, E., Okerlund, J., Hoef, C., Davis, J., & Shaer, O. (2015) · Augmented Human International Conference · doi:10.1145/2735711.2735797 · Cited by 5 · see citing works
Self-citationCarotid body transmitters actions on rabbit petrosal ganglion in vitro.
Alcayaga, J., Soto, C. R., Vargas, R. V., Ortíz, F. C., Arroyo, J., & Iturriaga, R. (2006) · PubMed · doi:10.1007/0-387-31311-7_51 · Cited by 4 · see citing works
Hincks, S. W., Bratt, S., Poudel, S., Phoha, V. V., Jacob, R. J. K., Dennett, D. C., & Hirshfield, L. (2017) · Cited by 4 · see citing works
Self-citation3D CNN based Multilevel Feature Fusion for Workload Estimation
Kwak, Y., Song, W., Min, B., & Kim, S. (2020) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci48061.2020.9061639 · Cited by 3 · see citing works
Toward Real-time Brain Sensing for Learning Assessment
Keating, S., Walker, E., Motupali, A., & Solovey, E. (2016) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2851581.2892496 · Cited by 3 · see citing works
Self-citationImpact of BCI-Informed Visual Effect Adaptation in a Walking Simulator
Chen, M., Solovey, E., & Smith, G. (2023) · International Conference on the Foundations of Digital Games (FDG) · doi:10.1145/3582437.3582448 · Cited by 2 · see citing works
Self-citationMaior, H. A., Pike, M., Wilson, M. L., & Sharples, S. (2013) · Lincoln Repository (University of Lincoln) · Cited by 2 · see citing works
The Adaptive User: Priming to Improve Interaction
Ottley, A., & Peck, E. M. (2013) · Cited by 1 · see citing works
Potential Health Benefit of Physical Embodiment in Elderly Counselling: A Longitudinal Case Study
Keshmiri, S., Sumioka, H., Okubo, M., Yamazaki, R., Nakae, A., & Ishiguro, H. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00183 · Cited by 1 · see citing works
Friedman, L., Liu, R., Walker, E., & Solovey, E. (2018) · doi:10.1145/3279810.3279854 · Cited by 1 · see citing works
Self-citationNIRS-Based BCIs: Reliability and Challenges
Strait, M. K., & Scheutz, M. J. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-07857-1_81
Self-citationLee, J., Seo, T.-S., & Ryu, H. B. (2014) · doi:10.5555/2729485.2729535
Quantifying Trust, Distrust, and Suspicion in Human-System Interactions
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Towards Bidirectional Brain-computer Interfaces that Use fNIRS and tDCS
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Roque, A. D. S., Santos, C. P., & Ferreira, M. H. (2015) · Brazilian Symposium on Human Factors in Computing Systems (IHC) · doi:10.1145/3148456.3148461
Enhancing Adaptive Human-Agent Teaming Systems with Functional Near-Infrared Spectroscopy
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Capturing Team Cognition: A Multimodal Dataset for Adaptive Collaborative Interfaces
Micek, C., Warnke, L., Rodrigues, L., Putze, F., & Solovey, E. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791607
Self-citationArnold, A., Crum, J. E., II, Čeko, M., & Hirshfield, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29548-4_7
- Authors
- Solovey, E., Schermerhorn, P., Scheutz, M., Sassaroli, A., Fantini, S., & Jacob, R.
- Affiliations
- Massachusetts Institute of Technology (US)
- Indiana University Bloomington (US)
- Tufts University (US)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2012-05-05
- Pages
- 2193-2202
- OpenAlex
- W2045724842
- Cited by
- 86
- References
- 32
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 6 · 2022: 3 · 2021: 1 · 2020: 11 · 2019: 7 · 2018: 4 · 2017: 7 · 2016: 4 · 2015: 13 · 2014: 14 · 2013: 10 · 2012: 1
- FWCI
- 23.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- National Science Foundation
- Computing Research Association
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- brain activation patterns, multitasking, human-robot interaction, dual-task performance
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