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Improving Object Segmentation by using EEG signals and Rapid Serial Visual Presentation


Eva Mohedano, Graham Healy, Kevin McGuinness, Xavier Giro-i-Nieto, Noel O'Connor, Alan Smeaton

Publication Type: 
Refereed Original Article
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.
Digital Object Identifer (DOI): 
Publication Status: 
Date Accepted for Publication: 
Wednesday, 1 July, 2015
Publication Date: 
Multimedia Tools and Applications
Research Group: 
Dublin City University (DCU)
Open access repository: