虚拟现实中高认知负荷下从脑电图解码决策正确性：对协作脑机接口团队的启示
Decoding Decision Correctness from EEG Under High Cognitive Workload in Virtual Reality: Implications for Collaborative Brain-Computer Interface Teams
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- Queen's University Belfast（贝尔法斯特女王大学）
- Liverpool John Moores University（利物浦约翰摩尔斯大学）
- Defence Science Technology Laboratory（国防科学技术实验室）
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中文总结 AI 辅助
该研究在虚拟现实高认知负荷下，利用空间协方差脑电图特征作为决策正确性的先发信号，发现其可提升团队争议试次的决策准确率，为协作脑机接口团队的部署提供了负荷条件相关的启示。
中文摘要 AI 辅助
协作脑机接口（cBCI）为增强团队决策提供了极具前景的机制，但现有方法仅依赖决策做出并报告后才可用的证据，如反应时间或陈述的置信度，这将其用途限制为事后解释或否定决策，而非在决策最终确定前为团队的应对提供信息。我们测试了空间协方差脑电图特征是否能提供操作者决策正确性的真正先发信号（该信号在反应窗口内即可获取），以及此类信号是否依赖于认知负荷。采用连续虚拟现实目标检测任务，23名参与者完成了被试内负荷操作（高负荷vs.低负荷）。在团队层面，根据该先发神经信号对投票进行加权（该信号在反应确定前即可获取），在高负荷下，对于有争议（票数平分）的试次，随着团队规模从2增加到16，准确率从57%大幅提升至88%，但在低负荷下则会产生不利影响。关键的是，这种优势甚至优于事后行为信号：置信度总体上是最强的单团队层面信号，但根据定义，它无法为仍在进行中的决策提供信息，而神经信号可以。这些发现表明，基于脑电图的决策可靠性信号并非通用的团队增强工具，而是受负荷条件制约的工具，对cBCI系统何时以及如何在运营团队中部署具有明确启示。
英文摘要
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 team's response before it is finalised. We tested whether spatial-covariance EEG features could instead provide a genuinely pre-emptive signal of an operator's decision correctness, available within the response window itself, and whether such a signal depends on cognitive workload. Using a continuous virtual reality target-detection task, participants (N = 23) completed a within-subject workload manipulation (High vs. Low). 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. 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.
