title: The impact of expertise on brain computer interface based salient image retrieval.
abstract: Autonomous decision making modules in computer vision application allow recognition and classification of different objects, persons, and events in images and video sequences and also make it possible to classify different sensor readings (e.g. images) according to their scientific saliencies. In this paper, we propose a new approach to create the training set for these algorithms by retrieving salient images using electroencephalogram (EEG) and brain computer interface (BCI) and rapid image presentation. To this end, two groups of subjects, namely, expert and novice subjects were asked to participate in our experiments. We show that a relatively high retrieval accuracy can be achieved for most of the subjects. Furthermore, to assess the impact of expertise on the retrieval process, we study their EEG signals separately and show that there is a clear difference in their brainwaves while observing salient images.
authors.affiliation: Ecole Polytechnique Fédérale de Lausanne (EPFL), Institute of Electrical Engineering (IEL), Multimedia Signal Processing Group (MMSPG), Switzerland.
authors.forename: Ashkan
authors.initials: A
authors.lastname: Yazdani
authors.forename: Jean-Marc
authors.initials: JM
authors.lastname: Vesin
authors.forename: Dario
authors.initials: D
authors.lastname: Izzo
authors.forename: Christos
authors.initials: C
authors.lastname: Ampatzis
authors.forename: Touradj
authors.initials: T
authors.lastname: Ebrahimi
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005103:Expert Systems; D005260:Female; D006801:Humans; D008297:Male; D008570:Memory, Short-Term / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface; D014793:Visual Cortex / Q000502:physiology*