Collaborative brain-computer interface for aiding decision-making.

We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controll...

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Main Authors: Riccardo Poli, Davide Valeriani, Caterina Cinel
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4114490?pdf=render
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spelling doaj-cf9d0cbe558742919f42f19673d677032020-11-25T00:13:11ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0197e10269310.1371/journal.pone.0102693Collaborative brain-computer interface for aiding decision-making.Riccardo PoliDavide ValerianiCaterina CinelWe look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.http://europepmc.org/articles/PMC4114490?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Riccardo Poli
Davide Valeriani
Caterina Cinel
spellingShingle Riccardo Poli
Davide Valeriani
Caterina Cinel
Collaborative brain-computer interface for aiding decision-making.
PLoS ONE
author_facet Riccardo Poli
Davide Valeriani
Caterina Cinel
author_sort Riccardo Poli
title Collaborative brain-computer interface for aiding decision-making.
title_short Collaborative brain-computer interface for aiding decision-making.
title_full Collaborative brain-computer interface for aiding decision-making.
title_fullStr Collaborative brain-computer interface for aiding decision-making.
title_full_unstemmed Collaborative brain-computer interface for aiding decision-making.
title_sort collaborative brain-computer interface for aiding decision-making.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2014-01-01
description We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.
url http://europepmc.org/articles/PMC4114490?pdf=render
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AT davidevaleriani collaborativebraincomputerinterfaceforaidingdecisionmaking
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