Rapid image analysis using neural signals

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Rapid image analysis using neural signals

Full text

Pdf (1.57 MB)

Source

Conference on Human Factors in Computing Systems archive
CHI '08 extended abstracts on Human factors in computing systems table of contents

Florence, Italy

SESSION: Works in progress table of contents

Pages 3309-3314

Year of Publication: 2008

ISBN:978-1-60558-012-X

Authors

Santosh Mathan	Honeywell Laboratories, Redmond, WA, USA
Deniz Erdogmus	Oregon Health and Science University, Beaverton, OR, USA
Yonghong Huang	Oregon Health and Science University, Beaverton, OR, USA
Misha Pavel	Oregon Health and Science University, Beaverton, OR, USA
Patricia Ververs	Honeywell Laboratories, Columbia, MD, USA
James Carciofini	Honeywell Laboratories, Minneapolis, MN, USA
Michael Dorneich	Honeywell Laboratories, Minneapolis, MN, USA
Stephen Whitlow	Honeywell Laboratories, Minneapolis, MN, USA

Sponsors

ACM: Association for Computing Machinery
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 16, Downloads (12 Months): 78, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1358628.1358849 What is a DOI?

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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Delorme, A. and Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics J Neuroscience Methods, 134:9--21, 2004
	2	Richard O. Duda , Peter E. Hart , David G. Stork, Pattern Classification (2nd Edition), Wiley-Interscience, 2000
	3	Gerson, A. D., Parra, L. C. and Sajda, P. (2005) Cortical Origins of Response Time Variability during Rapid Discrimination of Visual Objects, NeuroImage, 28 (2) 326--341.
	4	Makeig, S., Westerfield, M., Jung, T-P., Enghoff, S. and Townsend, J. (2002) Dynamic Brain Sources of Visual Evoked Responses. Science 295: 690--693
	5	Santosh Mathan , Stephen Whitlow , Deniz Erdogmus , Misha Pavel , Patricia Ververs , Michael Dorneich, Neurophysiologically driven image triage: a pilot study, CHI '06 extended abstracts on Human factors in computing systems, April 22-27, 2006, Montréal, Québec, Canada [doi>10.1145/1125451.1125657]
	6	Thorpe, S., Fize, D. and Marlot, C.(1996). Speed of Processing in the Human Visual System. Nature, 381, 520--522