  P300-based brain-computer interfaces (BCIs) are often trained per-user and
per-application space. Training such models requires ground truth knowledge of
target and non-target stimulus categories during model training, which imparts
bias into the model. Additionally, not all non-targets are created equal; some
may contain visual features that resemble targets or may otherwise be visually
salient. Current research has indicated that non-target distractors may elicit
attenuated P300 responses based on the perceptual similarity of these
distractors to the target category. To minimize this bias, and enable a more
nuanced analysis, we use a generalized BCI approach that is fit to neither user
nor task. We do not seek to improve the overall accuracy of the BCI with our
generalized approach; we instead demonstrate the utility of our approach for
identifying target-related image features. When combined with other intelligent
agents, such as computer vision systems, the performance of the generalized
model equals that of the user-specific models, without any user specific data.
