title: Impact of target probability on single-trial EEG target detection in a difficult rapid serial visual presentation task.
abstract: 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.
authors.affiliation: Department of Psychological & Brain Sciences, and Institute for Collaborative Biotechnologies, University of California Santa Barbara, Santa Barbara, CA 93106-9660, USA.
authors.forename: Hubert
authors.initials: H
authors.lastname: Cecotti
authors.forename: Joyce
authors.initials: J
authors.lastname: Sato-Reinhold
authors.forename: Jocelyn L
authors.initials: JL
authors.lastname: Sy
authors.forename: James C
authors.initials: JC
authors.lastname: Elliott
authors.forename: Miguel P
authors.initials: MP
authors.lastname: Eckstein
authors.forename: Barry
authors.initials: B
authors.lastname: Giesbrecht
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods*; D001330:Electronic Data Processing; D004867:Equipment Design; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D011336:Probability; D012372:ROC Curve; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D014785:Vision, Ocular