Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The Amsterdam Library of Object Images
International Journal of Computer Vision
Human-aided computing: utilizing implicit human processing to classify images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Recent proof-of-concept research has appeared showing the applicability of Brain Computer Interface (BCI) technology in combination with the human visual system, to classify images. The basic premise here is that images that arouse a participant's attention generate a detectable response in their brainwaves, measurable using an electroencephalograph (EEG). When a participant is given a target class of images to search for, each image belonging to that target class presented within a stream of images should elicit a distinctly detectable neural response. Previous work in this domain has primarily focused on validating the technique on proof of concept image sets that demonstrate desired properties and on examining the capabilities of the technique at various image presentation speeds. In this paper we expand on this by examining the capability of the technique when using a reduced number of channels in the EEG, and its impact on the detection accuracy.