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...
Main Authors: | , , |
---|---|
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 |
id |
doaj-cf9d0cbe558742919f42f19673d67703 |
---|---|
record_format |
Article |
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 |
work_keys_str_mv |
AT riccardopoli collaborativebraincomputerinterfaceforaidingdecisionmaking AT davidevaleriani collaborativebraincomputerinterfaceforaidingdecisionmaking AT caterinacinel collaborativebraincomputerinterfaceforaidingdecisionmaking |
_version_ |
1725395957168734208 |