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Optimising the number of channels in EEG-augmented image search
Healy, Graham; Smeaton, Alan F.
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.
Keyword(s): Image processing; Brain computer interface; BCI; image search; electroencephalograph; EEG; P300
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Healy, Graham ORCID: 0000-0001-6429-6339 <> and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 <> (2011) Optimising the number of channels in EEG-augmented image search. In: THe 25th BCS Conference on Human-Computer Interaction (HCI), 4th - 8th July 2011, Newcastle-upon-Tyne, UK.
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First Indexed: 2011-06-18 05:14:07 Last Updated: 2019-02-09 06:49:01