Exploring EEG for object detection and retrieval

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dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.contributor.author	Mohedano Robles, Eva
dc.contributor.author	Salvador Aguilera, Amaia
dc.contributor.author	Porta, Sergi
dc.contributor.author	Giró Nieto, Xavier
dc.contributor.author	Healy, Graham
dc.contributor.author	McGuinness, Kevin
dc.contributor.author	O'Connor, Noel
dc.contributor.author	Smeaton, Alan F.
dc.date	2015
dc.identifier.citation	Mohedano, E., Salvador, A., Porta, S., Giro, X., Healy, G., McGuinness, K., O'Connor, N., Smeaton, A. Exploring EEG for object detection and retrieval. A: ACM International Conference on Multimedia Retrieval. "ICMR '15 Proceedings of the 5th ACM on International Conference on Multimedia Retrieval". Shanghai: Association for Computing Machinery (ACM), 2015, p. 591-594.
dc.identifier.citation	978-1-4503-3274-3
dc.identifier.citation	10.1145/2671188.2749368
dc.identifier.uri	http://hdl.handle.net/2117/82503
dc.language.iso	eng
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	http://dl.acm.org/citation.cfm?doid=2671188.2749368
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject	Brain-computer interfaces
dc.subject	Electroencephalography
dc.subject	Brain-computer interfaces
dc.subject	Electroencephalography
dc.subject	Rapid serial visual presentation
dc.subject	Classification
dc.subject	Instance retrieval
dc.subject	Interfícies cervell-ordinador
dc.subject	Electroencefalografia
dc.title	Exploring EEG for object detection and retrieval
dc.type	info:eu-repo/semantics/draft
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	This paper explores the potential for using Brain Computer Interfaces (BCI) as a relevance feedback mechanism in content-based image retrieval. Several experiments are performed using a rapid serial visual presentation (RSVP) of images at different rates (5Hz and 10Hz) on 8 users with different degrees of familiarization with BCI and the dataset. We compare the feedback from the BCI and mouse-based interfaces in a subset of TRECVid images, finding that, when users have limited time to annotate the images, both interfaces are comparable in performance. Comparing our best users in a retrieval task, we found that EEG-based relevance feedback can outperform mouse-based feedback.
dc.description.abstract	Peer Reviewed

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