In our application, the goal is to search through a large image to find all instances of a pre-specified, high-valued target. One approach taken to increase the throughput of this image search task is to: chop the large image into numerous small images, display them to a user at high rates one-at-atime, and then search the simultaneously-recorded EEG data for neural activity that signifies that the user detected an instance of the target. The temporal efficiency of this EEGbased system is reduced by the overhead, which increases as the number of electrodes increases. Hence, we wish to find a minimal set of electrodes that ideally maintains the detection performance. In order to inform the design of future EEGbased image search systems, in this paper we find the 12 out of 32/64 most important electrodes for detection using 5 different feature selection methods. The optimal set includes all 5 occipital and the 2 most frontal electrodes.