Analyzing P300 Distractors for Target Reconstruction

TL;DR

This paper introduces a generalized P300 BCI approach that identifies target-related features without user-specific training, reducing bias and enabling better integration with computer vision systems.

Contribution

The study presents a generalized BCI method that does not require user-specific data, facilitating unbiased analysis of distractors and target features.

Findings

Generalized BCI matches user-specific models in performance

Method reduces training bias in P300-based BCIs

Enables integration with computer vision systems

Abstract

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.