ArticleScientific reports2017
Group Augmentation in Realistic Visual-Search Decisions via a Hybrid Brain-Computer Interface.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Group-member selection for RSVP-based collaborative brain-computer interfaces.Frontiers in neuroscience · 2024Article
- Decoding of the neural representation of the visual RGB color model.PeerJ. Computer science · 2023Article
- Multimodal collaborative brain-computer interfaces aid human-machine team decision-making in a pandemic scenario.Journal of neural engineering · 2022Article
- Enhancing performance with multisensory cues in a realistic target discrimination task.PloS one · 2022Article
- A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets.Computational intelligence and neuroscience · 2022Article
- The present and future of neural interfaces.Frontiers in neurorobotics · 2022Article
- Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.Brain computer interfaces (Abingdon, England) · 2022Article
- An Optimal Transport Based Transferable System for Detection of Erroneous Somato-Sensory Feedback from Neural Signals.Brain sciences · 2021Article
- Anytime collaborative brain-computer interfaces for enhancing perceptual group decision-making.Scientific reports · 2021Article
- A Cross-Session Dataset for Collaborative Brain-Computer Interfaces Based on Rapid Serial Visual Presentation.Frontiers in neuroscience · 2020Article
- Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects.Frontiers in human neuroscience · 2019Review
- Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Groups have increased sensing and cognition capabilities that typically allow them to make better decisions. However, factors such as communication biases and time constraints can lead to less-than-optimal group decisions. In this study, we use a hybrid Brain-Computer Interface (hBCI) to improve the performance of groups undertaking a realistic visual-search task. Our hBCI extracts neural information from EEG signals and combines it with response times to build an estimate of the decision confidence. This is used to weigh individual responses, resulting in improved group decisions. We compare the performance of hBCI-assisted groups with the performance of non-BCI groups using standard majority voting, and non-BCI groups using weighted voting based on reported decision confidence. We also investigate the impact on group performance of a computer-mediated form of communication between members. Results across three experiments suggest that the hBCI provides significant advantages over non-BCI decision methods in all cases. We also found that our form of communication increases individual error rates by almost 50% compared to non-communicating observers, which also results in worse group performance. Communication also makes reported confidence uncorrelated with the decision correctness, thereby nullifying its value in weighing votes. In summary, best decisions are achieved by hBCI-assisted, non-communicating groups.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.