This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When a block of pixels is displayed, we estimate the probability of that block containing the object of interest using a score based on EEG activity. After several such blocks are displayed in rapid visual serial presentation, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study extends our previous work that showed how BCI and simple EEG analysis are useful in locating object boundaries in images