Integrating EEG-SVM Confidence and RT via cBCI Enhances Team Decisions in a VR Drone Task

Abstract Optimizing team decision-making by appropriately weighting individual contributions is a significant challenge. Collaborative Brain-Computer Interfaces (cBCIs) offer a novel approach by integrating neurophysiological and behavioral data. This study evaluated a cBCI system incorporating EEG-derived SVM decision confidence, response times (RT), and subjective confidence ratings to enhance team accuracy in a VR drone target-detection task, particularly under varying cognitive workload (Low vs. High). Seventeen participants performed the task; individual SVMs were trained, and team performance (N = 2–8 members) was simulated using diverse aggregation methods. Under High Workload, mixed cBCI methods (e.g., combining subjective and SVM confidence) significantly improved team accuracy, surpassing even the best individual's average performance (e.g., N = 8: 98.8% vs. 94.2%). This synergistic benefit was minimal under Low Workload due to ceiling effects in individual performance. These cBCI enhancements were evident for EEG data from both pre- and post-decision epochs. The findings demonstrate that cBCIs can markedly improve team decision-making in demanding contexts, facilitating a "superorganism" effect where team capabilities exceed those of the best individual.