Impact of target probability on single-trial EEG target detection in a difficult rapid serial visual presentation task

In non-invasive brain-computer interface (BCI), the analysis of event-related potentials (ERP) has typically focused on averaged trials, a current trend is to analyze single-trial evoked response individually with new approaches in pattern recognition and signal processing. Such single trial detection requires a robust response that can be detected in a variety task conditions. Here, we investigated the influence of target probability, a key factor known to influence the amplitude of the evoked response, on single trial target classification in a difficult rapid serial visual presentation (RSVP) task. Our classification approach for detecting target vs. non target responses, considers spatial filters obtained through the maximization of the signal to signal-plus-noise ratio, and then uses the resulting information as inputs to a Bayesian discriminant analysis. The method is evaluated across eight healthy subjects, on four probability conditions (P=0.05, 0.10, 0.25, 0.50). We show that the target probability has a statistically significant effect on both the behavioral performance and the target detection. The best mean area under the ROC curve is achieved with P=0.10, AUC=0.82. These results suggest that optimal performance of ERP detection in RSVP tasks is critically dependent on target probability.