For the Sixth Annual Machine Learning for Signal Processing competition, sponsored by Nokia and PASCAL2, entrants were asked to develop a classifier, with optional feature extraction, that uses electroencephalography (EEG) data collected during an image presentation (visual odd-ball) task and optimally determines whether each image presented to a user contains or does not contain a pre-specified target. In this paper, we (the organizers of the competition) briefly describe the application, the data, the rules, and the outcomes of the competition. A total of 35 teams entered the contest. Training data were provided. The entries were tested using disjoint test data. The three teams with the best performing entries describe the approach they used in three separate companion papers, all of which appear in this year's conference proceedings.