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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-a-time, 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 EEG-based 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 EEG-based 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.