Rapid image analysis using neural signals
Abstract
The problem of extracting information from large collections of imagery is a challenge with few good solutions. Computers typically cannot interpret imagery as effectively as humans can, and manual analysis tools are slow. The research reported here explores the feasibility of speeding up manual image analysis by tapping into split second perceptual judgments using electroencephalograph sensors. Experimental results show that a combination of neurophysiological signals and overt physical responses--detected while a user views imagery in high speed bursts of approximately 10 images per second--provide a basis for detecting targets within large image sets. Results show an approximately six-fold, statistically significant, reduction in the time required to detect targets at high accuracy levels compared to conventional broad-area image analysis.
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RSVP-based BCI can be beneficial to applications where a large number of images need to be reviewed by professionals but cannot be well analyzed by computers.
“Different from the P300 speller, the RSVP-based BCI can be beneficial to more applications where a large number of images need to be reviewed by professionals but are unable to be well analyzed by computers [15], [16], [17].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [16]
Cites
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Speed of processing in the human visual system
Thorpe, S., Fize, D., & Marlot, C. (1996) · Nature · doi:10.1038/381520a0 · Cited by 3,538 · see citing works
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Dynamic Brain Sources of Visual Evoked Responses
Makeig, S., Westerfield, M., Jung, T.-P., Enghoff, S., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2002) · Science · doi:10.1126/science.1066168 · Cited by 1,630 · see citing works
Cortical origins of response time variability during rapid discrimination of visual objects
Gerson, A. D., Parra, L. C., & Sajda, P. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.06.026 · Cited by 103 · see citing works
Neurophysiologically driven image triage
Mathan, S. A., Whitlow, S. D., Erdoğmuş, D., et al. (2006) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1125451.1125657 · Cited by 17 · see citing works
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A review of rapid serial visual presentation-based brain–computer interfaces
Lees, S., Dayan, N., Cecotti, H., McCullagh, P., Maguire, L., Lotte, F., & Coyle, D. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa9817 · Cited by 149 · see citing works
Marathe, A. R., Lawhern, V. J., Wu, D., Slayback, D., & Lance, B. J. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2502323 · Cited by 64 · see citing works
Matran-Fernandez, A., & Poli, R. (2017) · PLOS ONE · doi:10.1371/journal.pone.0178498 · Cited by 45 · see citing works
A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection
Wang, H., Wang, Z., Sun, Y., Yuan, Z., Xu, T., & Li, J. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3415474 · Cited by 20 · see citing works
“Different from the P300 speller, the RSVP-based BCI can be beneficial to more applications where a large number of images need to be reviewed by professionals but are unable to be well analyzed by computers [15], [16], [17].”
Method
- Authors
- Mathan, S., Erdogmus, D., Huang, Y., Pavel, M., Ververs, P., Carciofini, J., Dorneich, M., & Whitlow, S.
- Affiliations
- Honeywell (United States) (US)
- Oregon Health and Science University, Beaverton, OR, USA
- Oregon Health & Science University (US)
- Honeywell Laboratories, Minneapolis, MN, USA
- Venue
- CHI '08 Extended Abstracts on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2008-04-05
- Pages
- 3309-3314
- OpenAlex
- W1979774487
- Cited by
- 28
- References
- 7
- Citations by year
- 2025: 1 · 2024: 1 · 2020: 1 · 2019: 1 · 2017: 2 · 2015: 2 · 2014: 1 · 2013: 4 · 2012: 3
- FWCI
- 7.75
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- EEG, neural signals, perceptual judgment, target detection, image collections, human-computer interaction, visual perception
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