000188265 001__ 188265
000188265 005__ 20180913062005.0
000188265 0247_ $$2doi$$a10.3217/978-3-85125-260-6-62
000188265 037__ $$aCONF
000188265 245__ $$aIterative EEG-based Natural Image Search under RSVP
000188265 269__ $$a2013
000188265 260__ $$aGraz, Austria$$bGraz University of Technology Publishing House$$c2013
000188265 336__ $$aConference Papers
000188265 520__ $$aThis work extends previous studies on using EEG decoding for automatic image retrieval. We propose an iterative way to integrate the information obtained from the EEG decoding and image processing methods. In the light of real-world BCI applications, we demonstrated that a limited number of EEG channels provide sufficient information about the subject’s preference to be exploited in image retrieval by the proposed synergistic scenario. Furthermore, to meet a more realistic scenario we used natural images (i.e., images of objects in their natural environment).
000188265 6531_ $$aEEG
000188265 6531_ $$aBCI
000188265 6531_ $$aSingle-Trial Classification
000188265 6531_ $$aRSVP
000188265 6531_ $$aImage Retrieval
000188265 700__ $$0242181$$aUscumlic, Marija$$g182483
000188265 700__ $$aChavarriaga, Ricardo
000188265 700__ $$0240030$$aMillán, José del R.$$g149175
000188265 7112_ $$a5th International BCI Meeting$$cAsilomar Conference Center, Pacific Grove, California$$dJune 3-7, 2013
000188265 773__ $$tProceedings of the Fifth International Brain-Computer Interface Meeting 2013
000188265 8564_ $$s101451$$uhttps://infoscience.epfl.ch/record/188265/files/062.pdf$$yn/a$$zn/a
000188265 909C0 $$0252018$$pCNBI$$xU12103
000188265 909C0 $$0252517$$pCNP$$xU12599
000188265 909CO $$ooai:infoscience.tind.io:188265$$pconf$$pSTI
000188265 917Z8 $$x149175
000188265 917Z8 $$x149175
000188265 917Z8 $$x149175
000188265 917Z8 $$x137762
000188265 937__ $$aEPFL-CONF-188265
000188265 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000188265 980__ $$aCONF