Decoding decision correctness from EEG under high cognitive workload in virtual reality: implications for collaborative brain-computer interface teams

Introduction Collaborative Brain-Computer Interfaces (cBCIs) offer a promising mechanism to augment team decision‐making, but existing approaches rely exclusively on evidence available only after a decision has been made and reported, such as reaction time or stated confidence. This limits their use to explaining or discounting a decision after the fact, rather than informing a teams response before it is finalised. We tested whether spatial‐covariance EEG features could instead provide a genuinely pre-emptive signal of an operators decision correctness, available within the response window itself, and whether such a signal depends on cognitive workload. Methods Using a continuous virtual reality target-detection task, participants (N = 23) completed a within-subject visual workload manipulation (High vs. Low), implemented via a Degraded Visual Environment. Results A Riemannian tangent‐space classifier, trained on a strictly motor‐safe EEG window preceding each response, decoded Correct from Incorrect decisions with above‐chance accuracy under High Workload (61.4%, p = .0014, N = 23) but not reliably under Low Workload (N = 17), where near‐ceiling behavioural accuracy left too few error trials for valid classification. Ablation analysis showed the signal was carried predominantly by posterior occipito‐parietal channels, consistent with a perceptual rather than motor origin. At the team level, weighting votes by this pre-emptive neural signal, available before a response is committed, produced substantial accuracy gains on contested (evenly‐split) trials under High Workload (57% to 88% as team size increased from 2 to 16), but was actively detrimental under Low Workload. Discussion Critically, this advantage held even against post‐hoc behavioural signals: confidence was the strongest single team‐level signal overall, but by definition cannot inform a decision still in progress, whereas the neural signal can. These findings indicate that EEG‐based decision‐reliability signals are not a general‐purpose team augmentation tool, but a workload‐conditional one, with clear implications for when and how cBCI systems should be deployed in operational teams.