Multimedia Tools and Applications

, Volume 74, Issue 22, pp 10137–10159 | Cite as

Improving object segmentation by using EEG signals and rapid serial visual presentation

  • Eva Mohedano
  • Graham Healy
  • Kevin McGuinness
  • Xavier Giró-i-Nieto
  • Noel E. O’Connor
  • Alan F. Smeaton
Article

Abstract

This paper extends our previous work on the potential of EEG-based brain computer interfaces to segment salient objects in images. The proposed system analyzes the Event Related Potentials (ERP) generated by the rapid serial visual presentation of windows on the image. The detection of the P300 signal allows estimating a saliency map of the image, which is used to seed a semi-supervised object segmentation algorithm. Thanks to the new contributions presented in this work, the average Jaccard index was improved from 0.47 to 0.66 when processed in our publicly available dataset of images, object masks and captured EEG signals. This work also studies alternative architectures to the original one, the impact of object occupation in each image window, and a more robust evaluation based on statistical analysis and a weighted F-score.

Keywords

Brain-computer interfaces Electroencephalography Rapid serial visual presentation Object segmentation Interactive segmentation GrabCut algorithm 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Eva Mohedano
    • 1
  • Graham Healy
    • 1
  • Kevin McGuinness
    • 1
  • Xavier Giró-i-Nieto
    • 2
  • Noel E. O’Connor
    • 1
  • Alan F. Smeaton
    • 1
  1. 1.Insight Center for data AnalyticsDublin City UniversityDublinIreland
  2. 2.Image Processing GroupUniversitat Politcnica de CatalunyaCatalunyaSpain

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