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Decoding Decision Correctness from EEG Under High Cognitive Workload in Virtual Reality: Implications for Collaborative Brain-Computer Interface Teams — arXiv:2609.02436

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02436
License (as archived): CC0-1.0
Archived: 2026-10-17T04:44:30.743Z
SHA-256 of the archived text: 17b9535b6f5d903f84586a82ace87e3a374fac20258e4449d5576add130f21dc
Stable URL of this page: https://metraipolis.com/documento/dc_fdff65e669f3

How to cite

Decoding Decision Correctness from EEG Under High Cognitive Workload in Virtual Reality: Implications for Collaborative Brain-Computer Interface Teams — arXiv:2609.02436 — arXiv (en), https://arxiv.org/abs/2609.02436 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fdff65e669f3 (Metraipolis archive dc_fdff65e669f3, sha256 17b9535b6f5d903f84586a82ace87e3a374fac20258e4449d5576add130f21dc)

BibTeX

@misc{dcfdff65e669f3,
  title = {Decoding Decision Correctness from EEG Under High Cognitive Workload in Virtual Reality: Implications for Collaborative Brain-Computer Interface Teams — arXiv:2609.02436},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02436},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fdff65e669f3; sha256 17b9535b6f5d903f84586a82ace87e3a374fac20258e4449d5576add130f21dc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02436. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-03 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2609.02436 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T04:09:22.113Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).