NDLI: High-throughput image search via single-trial event detection in a rapid serial visual presentation task

Content Provider	IEEE Xplore Digital Library
Author	Sajda, P. Gerson, A. Parra, L.
Copyright Year	2003
Description	Author affiliation: Dept. of Biomed. Eng., Columbia Univ., New York, NY, USA (Sajda, P.; Gerson, A.)
Abstract	We describe a method, using linear discrimination, for detecting single-trial EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (87 electrodes) during the rapid presentation (50-200 msec per image) of natural images. Subjects were instructed to release a button when they recognized a target image (an image with a person/people). Trials consisted of 100 images each, with a 50% chance of a single target being in a trial. Subject EEG was analyzed on a single-trial basis with an optimal spatial linear discriminator learned at multiple time windows after the presentation of an image. Linear discrimination enables the estimation of a forward model and thus allows for an approximate localization of the discriminating activity. Results show multiple loci for discriminating activity (e.g. motor and visual). Using these detected EEG signatures, we show that in many cases we can detect targets more accurately than the overt response (button release) and that such signatures can be used to prioritize images for high-throughput search.
Starting Page	7
Ending Page	10
File Size	399407
Page Count	4
File Format	PDF
ISBN	0780375793
DOI	10.1109/CNE.2003.1196297
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-03-20
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrodes Image recognition Image analysis Event detection Layout Object detection Brain modeling Electroencephalography Object recognition Biomedical engineering
Content Type	Text
Resource Type	Article

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