title: Combining computer and human vision into a BCI: can the whole be greater than the sum of its parts?
abstract: Our group has been investigating the development of BCI systems for improving information delivery to a user, specifically systems for triaging image content based on what captures a user's attention. One of the systems we have developed uses single-trial EEG scores as noisy labels for a computer vision image retrieval system. In this paper we investigate how the noisy nature of the EEG-derived labels affects the resulting accuracy of the computer vision system. Specifically, we consider how the precision of the EEG scores affects the resulting precision of images retrieved by a graph-based transductive learning model designed to propagate image class labels based on image feature similarity and sparse labels.
authors.affiliation: Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA. ep2473@columbia.edu
authors.forename: Eric A
authors.initials: EA
authors.lastname: Pohlmeyer
authors.forename: David C
authors.initials: DC
authors.lastname: Jangraw
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.forename: Shih-Fu
authors.initials: SF
authors.lastname: Chang
authors.forename: Paul
authors.initials: P
authors.lastname: Sajda
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D004569:Electroencephalography; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval; D008328:Man-Machine Systems; D012372:ROC Curve; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface; D014796:Visual Perception