Closing the loop in cortically-coupled computer vision: a brain-computer interface for searching image databases

We describe a closed-loop brain-computer interface that re-ranks an image database by iterating between user generated 'interest' scores and computer vision generated visual similarity measures. The interest scores are based on decoding the electroencephalographic (EEG) correlates of targe...

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Bibliographic Details
Published in:Journal of neural engineering Vol. 8; no. 3; p. 036025
Main Authors: Pohlmeyer, Eric A, Wang, Jun, Jangraw, David C, Lou, Bin, Chang, Shih-Fu, Sajda, Paul
Format: Journal Article
Language:English
Published: England 01.06.2011
Subjects:
Artificial Intelligence
Database Management Systems
Databases, Factual
Image Interpretation, Computer-Assisted - methods
Pattern Recognition, Automated - methods
Radiology Information Systems
User-Computer Interface
ISSN:1741-2552, 1741-2552
Online Access:Get more information
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Summary:We describe a closed-loop brain-computer interface that re-ranks an image database by iterating between user generated 'interest' scores and computer vision generated visual similarity measures. The interest scores are based on decoding the electroencephalographic (EEG) correlates of target detection, attentional shifts and self-monitoring processes, which result from the user paying attention to target images interspersed in rapid serial visual presentation (RSVP) sequences. The highest scored images are passed to a semi-supervised computer vision system that reorganizes the image database accordingly, using a graph-based representation that captures visual similarity between images. The system can either query the user for more information, by adaptively resampling the database to create additional RSVP sequences, or it can converge to a 'done' state. The done state includes a final ranking of the image database and also a 'guess' of the user's chosen category of interest. We find that the closed-loop system's re-rankings can substantially expedite database searches for target image categories chosen by the subjects. Furthermore, better reorganizations are achieved than by relying on EEG interest rankings alone, or if the system were simply run in an open loop format without adaptive resampling.
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ISSN:1741-2552
1741-2552
DOI:10.1088/1741-2560/8/3/036025