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Research Article
Saugat Bhattacharyya
University of Ulster
Corresponding Author
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In this paper we present and test collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of group decision-making in realistic situations. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.
Keywords
Brain-Computer Interfaces (cBCIs), Event-Related Potentials (ERPs)
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CURRENT STATUS: Published
View the published article at Scientific Reports.
Version 1
Posted 15 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 28 Apr, 2021
Reviews received
Received 18 Mar, 2021
Reviewers agreed
On 18 Mar, 2021
Reviewers agreed
On 18 Feb, 2021
Reviewers invited
Invitations sent on 31 Jan, 2021
Editor assigned
On 31 Jan, 2021
Editor invited
On 14 Dec, 2020
Submission checks complete
On 14 Dec, 2020
First submitted
On 22 Nov, 2020
Metrics
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PDF Downloads: ...
HTML Views: ...
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See the published version of this preprint at Scientific Reports.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Saugat Bhattacharyya
University of Ulster
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Scientific Reports.
Version 1
Posted 15 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 28 Apr, 2021
Reviews received
Received 18 Mar, 2021
Reviewers agreed
On 18 Mar, 2021
Reviewers agreed
On 18 Feb, 2021
Reviewers invited
Invitations sent on 31 Jan, 2021
Editor assigned
On 31 Jan, 2021
Editor invited
On 14 Dec, 2020
Submission checks complete
On 14 Dec, 2020
First submitted
On 22 Nov, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Scientific Reports.
In this paper we present and test collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of group decision-making in realistic situations. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
This preprint is available for download as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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