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Exploring EEG for Object Detection and Retrieval

Authors: 

Eva Mohedano, Amaia Salvador, Sergi Porta, Xavier GirĂ³-i-Nieto, Kevin McGuinness, Graham Healy, Noel O'Connor, Alan Smeaton

Publication Type: 
Refereed Conference Meeting Proceeding
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 inter- faces in a subset of TRECVid images, finding that, when users have limited time to annotate the images, both inter- faces are comparable in performance. Comparing our best users in a retrieval task, we found that EEG-based relevance feedback can outperform mouse-based feedback.
Conference Name: 
The Annual ACM International Conference on Multimedia Retrieval (ICMR)
Digital Object Identifer (DOI): 
10.NA
Publication Date: 
23/06/2015
Conference Location: 
China
Research Group: 
Institution: 
Dublin City University (DCU)
Open access repository: 
Yes