The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Using a low-cost electroencephalograph for task classification in HCI research
Abstract
Modern brain sensing technologies provide a variety of methods for detecting specific forms of brain activity. In this paper, we present an initial step in exploring how these technologies may be used to perform task classification and applied in a relevant manner to HCI research. We describe two experiments showing successful classification between tasks using a low-cost off-the-shelf electroencephalograph (EEG) system. In the first study, we achieved a mean classification accuracy of 84.0% in subjects performing one of three cognitive tasks - rest, mental arithmetic, and mental rotation - while sitting in a controlled posture. In the second study, conducted in more ecologically valid setting for HCI research, we attained a mean classification accuracy of 92.4% using three tasks that included non-cognitive features: a relaxation task, playing a PC based game without opponents, and engaging opponents within the game. Throughout the paper, we provide lessons learned and discuss how HCI researchers may utilize these technologies in their work.
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Cites
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson, R., Miller, G. A., Ritter, W., Ruchkin, D. S., Rugg, M. D., et al. (2000) · Psychophysiology · doi:10.1111/1469-8986.3720127 · Cited by 2,157
Using mental load for managing interruptions in physiologically attentive user interfaces
Chen, D., & Vertegaal, R. (2004) · CHI '04 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/985921.986103 · Cited by 120
Examining task engagement in sensor-based statistical models of human interruptibility
Fogarty, J., Ko, A. J., Aung, H. H., Golden, E., Tang, K. P., & Hudson, S. E. (2005) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1054972.1055018 · Cited by 111
Cited by
Pay attention!: designing adaptive agents that monitor and improve user engagement
Szafir, D., & Mutlu, B. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2207679 · Cited by 304
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
“EEG has been used to discriminate between cognitive states [71, 83, 148], as a measurement for user experience evaluation [70], and as an input method [14, 238].”
Using psycho-physiological measures to assess task difficulty in software development
Fritz, T., Begel, A., Müller, S. C., Yigit-Elliott, S., & Züger, M. (2014) · Proceedings of the 36th International Conference on Software Engineering · doi:10.1145/2568225.2568266 · Cited by 262
Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines
Solovey, E. T., Girouard, A., Chauncey, K., Hirshfield, L. M., Sassaroli, A., Zheng, F., Fantini, S., & Jacob, R. J. K. (2009) · Proceedings of the 22nd annual ACM symposium on User interface software and technology · doi:10.1145/1622176.1622207 · Cited by 115
Interruptibility of Software Developers and its Prediction Using Psycho-Physiological Sensors
Züger, M., & Fritz, T. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702593 · Cited by 96
Metadata
- Authors
- Lee, J. C., & Tan, D. S.
- Venue
- Proceedings of the 19th annual ACM symposium on User interface software and technology
- Publisher
- ACM
- Published
- 2006-10-15
- Pages
- 81-90
- OpenAlex
- W2125228090
- Cited by
- 165
- References
- 4
- FWCI
- 14.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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